diff --git "a/ANE1T4oBgHgl3EQfDQMU/content/tmp_files/load_file.txt" "b/ANE1T4oBgHgl3EQfDQMU/content/tmp_files/load_file.txt" new file mode 100644--- /dev/null +++ "b/ANE1T4oBgHgl3EQfDQMU/content/tmp_files/load_file.txt" @@ -0,0 +1,1029 @@ +filepath=/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf,len=1028 +page_content='“ It’s a Match!' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' ” A Benchmark of Task Affinity Scores for Joint Learning Rapha¨el Azorin,1 Massimo Gallo,1 Alessandro Finamore, 1 Dario Rossi,1 Pietro Michiardi 2 1Huawei Research Center, France 2Eurecom, France first.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='last@huawei.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='com, first.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='last@eurecom.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='fr Abstract While the promises of Multi-Task Learning (MTL) are at- tractive, characterizing the conditions of its success is still an open problem in Deep Learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Some tasks may benefit from being learned together while others may be detrimental to one another.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' From a task perspective, grouping coopera- tive tasks while separating competing tasks is paramount to reap the benefits of MTL, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', reducing training and inference costs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Therefore, estimating task affinity for joint learning is a key endeavor.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Recent work suggests that the training condi- tions themselves have a significant impact on the outcomes of MTL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Yet, the literature is lacking of a benchmark to assess the effectiveness of tasks affinity estimation techniques and their relation with actual MTL performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In this paper, we take a first step in recovering this gap by (i) defining a set of affinity scores by both revisiting contributions from previous literature as well presenting new ones and (ii) benchmark- ing them on the Taskonomy dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Our empirical campaign reveals how, even in a small-scale scenario, task affinity scor- ing does not correlate well with actual MTL performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Yet, some metrics can be more indicative than others.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 1 Introduction For more than two decades since its inception (Caruana 1997), Multi-Task Learning (MTL) has been extensively studied by the Deep Learning community.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' For practition- ers interested in the best strategy to learn a collection of tasks, the promises of MTL are numerous and attractive.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' First, learning to solve several tasks simultaneously can be more cost-efficient from a model development and deploy- ment perspective.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Second, if the tasks learned together co- operate, MTL can even outperform its Single-Task Learning (STL) counterpart for the same computational cost (Stand- ley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' However, MTL potential advantages are tempered by the difficulty of estimating task affinity, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', identify tasks ben- efiting from joint learning, without testing all combinations of tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This calls for task affinity scores – to quantify a priori and at a cheap computational cost the potential ben- efit of learning tasks together.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The quest for the perfect affinity score is further exacerbated by MTL performance’s strong dependency on the learning context, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', the data and Copyright © 2023, Association for the Advancement of Artificial Intelligence.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2nd Workshop on Practical Deep Learning in the Wild.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' models used for training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' For instance, tasks cooperating in one learning context can result in competition when using slightly different data or models (Standley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Recent works (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Standley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020) have integrated this context-dependency when designing task grouping strategies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' While these approaches avoid a complete search across all task combinations, they still re- quire training and comparing some MTL models for the fi- nal network selection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Furthermore, those studies show that even in a small-scale scenario, MTL performance cannot be accurately predicted without actually performing MTL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Despite providing assessment of task affinity, previous literature lacks of a broader comparison of the associated scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In this work, we take a first step in recovering this gap by presenting an empirical comparison of several task affinity scoring techniques.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Some of these scores are inspired by previous literature ranging from Transfer Learn- ing to Multi-Task Learning: taxonomical distance (Zamir et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018), input attribution similarity (Song et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2019), representation similarity analysis (Dwivedi and Roig 2019), gradient similarity (Zhao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018) and gradient transfer- ence (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We benchmark an additional affinity score which is an original proposal: label injection.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We eval- uate all of them on the public Taskonomy dataset (Zamir et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018) which is a well-known large benchmark span- ning several Computer Vision tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Note that our objective is not to present a novel state-of-the-art MTL architecture but rather an objective benchmark of task affinity estimation techniques.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' More specifically we aim to understand if task affinity scores can (i) be used as proxy for true MTL perfor- mance and (ii) suggest the best partner task to improve the performance of a target task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' These scores and their discus- sion aim at helping practitioners gauge the benefit of MTL for their own set of tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In section 2, we review the state of the art on MTL affinity characterization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In section 3, we present the affinity scores selected for benchmarking and de- tail our evaluation protocol.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We present our results in sec- tion 4 and discuss the advantages and limitations of these scores in section 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Section 6 concludes the paper.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2 Background and related work In this section, we first review relevant work on MTL and task grouping, briefly present the Taskonomy dataset, and finally introduce task affinity characterization.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' arXiv:2301.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='02873v1 [cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='LG] 7 Jan 2023 Multi-Task Learning The promises of MTL are based on the assumption that cooperative tasks benefit from induc- tive transfer during joint learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' By being learned together, tasks are encouraged to share, at least partially, common rep- resentations, e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', the extracted feature vector at the model’s bottleneck, depending on the model architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The intu- ition is that some tasks might exhibit compatible goals and help one another during training through synergies, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', pos- itive transfer.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' However, tasks interference can still degrade performance if their respective updates become unaligned or contradictory during simultaneous learning i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', negative transfer through competition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' To mitigate these effects, two complementary lines of re- search both aim at reducing task interference and increasing task synergies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The first direction focuses on model design, hence crafting the model such that it is adapted to learn a certain set of tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In this case, the task set is fixed while the model is adapted to fit all the tasks under considera- tion.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Through hard parameter sharing, task weights can be adapted during training in order to balance their impact on the combined loss (Leang et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Pascal et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Alternative approaches focus on tuning gradients to mitigate task interference during MTL training (Chen et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Yu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Kendall, Gal, and Cipolla 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In soft param- eter sharing instead, parameters are segregated by task and the model is guided, during MTL learning, to only share in- formation when it is beneficial (Sun et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Misra et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The second research direction is more recent and fo- cuses on task grouping strategies by identifying cooperative tasks that can be grouped to be profitably learned together.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In this case, the model design is fixed while the task set is adapted i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', split into potentially overlapping subsets.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Re- cent works from (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021) and (Standley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020) show promising results as they succeed in stimulating pos- itive transfer by combining only tasks that are beneficial to one another.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' While these two research directions are com- plementary, our work is more in the scope of the latter as we benchmark affinity scores that should indicate if tasks benefit one another when learned together.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Task grouping Task grouping strategies aim at assigning tasks to models (that can be STL or MTL) in order to max- imize the total performance of all the tasks under consider- ation, given a computational budget.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' More formally, let us consider the following: a set of n tasks T = {t1, t2, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', tn} that need to be solved;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' a total computational budget of β Multiply-Add opera- tions;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' a set of k ≤ n models M = {m1, m2, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', mk}, each one associated with its respective amount of Multiply-Adds operations C = {c1, c2, .' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='..' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', ck}.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Task grouping aims at constructing M such that for all ti ∈ T there exists exactly one model mj ∈ M assigned to solve task ti at inference time, while respecting the com- putational budget �k j=1 cj ≤ β.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Thus, any model from M can learn an arbitrary number of tasks as long as each task is assigned to one and only one model at inference time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' To Figure 1: (left) A valid task grouping.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Model1 is assigned both Taska and Taskb for inference.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Model2 is assigned Taskc for inference and it uses Taskb as a cooperative task only during training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' (right) An invalid task grouping.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Model1 and Model2 are both assigned to solve Taskb at in- ference time.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' illustrate this point, we present valid and invalid task group- ings in fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The final objective of task grouping is to max- imize the aggregated test performance: P = � ti∈T P(ti|M), (1) where P(ti|M) denotes the performance1 of task ti using its assigned model from M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' It is worth mentioning that the task grouping problem differs from simple model selection as (i) the objective (aggregated performance) and constraints (to- tal cost) concern all tasks and models simultaneously and (ii) any model can learn an arbitrary number of tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The optimal task grouping is typically obtained by test- ing all task combinations within the computational budget.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Therefore, to be as efficient as possible, grouping strategies rely on task affinity estimates that guide the search of a so- lution in the task groups space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This approach might only identify sub-optimal task groups but it has a much a lower cost than an exhaustive search.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Sophisticated task grouping strategies are studied in (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021) in terms of per- formance and runtime.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Such strategies include Higher-Order Approximation from (Standley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020), gradients cosine similarity maximization and task transference approxima- tion from (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Our work complements this benchmark of grouping strategies as we are interested in assessing the strengths and weaknesses of the underlying affinity scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This includes an evaluation of the predictive quality of such scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Indeed, the perfect scoring technique should not only identify the best partner tasks, but also be a proxy of the true MTL performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Overall, we aim for a broader view of affinity scoring qualities with respect to what provided in the literature.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Taskonomy – the reference framework From a Trans- fer Learning perspective, Taskonomy (Zamir et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018) has been a successful attempt at clarifying transfer syner- gies between visual tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' From an MTL perspective, (Stan- dley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020) performs a broad empirical campaign on the same dataset to identify which visual tasks should be 1using a task-specific metric such as Intersection over Union for semantic segmentation or minimizing the model loss Taska Taska Modeli Modeli Taskb Taskb Model2 Model2 Taskc TaskcFigure 2: STL model schematic architecture.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' θB denotes the backbone weights and θH the head weights.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' trained together with MTL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In particular, they evaluate if learning a target task with a partner task could outperform learning the target task alone.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Thus, this framework quanti- fies task affinity as the performance gained on a task learned in MTL versus STL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' First, the authors show that coopera- tion between tasks is not symmetrical, as one task may ben- efit from another but not necessarily the opposite.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Second, by comparing MTL performance gains for the same pairs of tasks but learned in various settings i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', different dataset size or different MTL model capacity, they unveil the impact of the training context itself on task cooperation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Based on this framework, (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021) monitors the evolution of task affinities during MTL training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Their experimentation on the CelebA dataset (Liu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018) suggests that task cooperation evolves throughout training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Furthermore they also show that hyper-parameters such as the learning rate or the batch size can also affect cooperation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Those works provide an in-depth view of relevant MTL training dynamics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Our work complements these findings with an in-breath view across several affinity scoring tech- niques that integrate, at varying degrees, data, model and hyper-parameters dependencies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Task affinity We group the methods aiming at quantify- ing task affinity for MTL under the term “affinity scores” for short and break them down into three main categories depending on their requirements for computation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Model-agnostic affinity scores are computed using solely the data at hand.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This may be accomplished using nomencla- tures or taxonomies to loosely relate tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' For example, Ob- ject classification and Semantic segmentation are both con- sidered to be semantic tasks while Depth estimation is a 3-D task (Zamir et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This can also be more sophisticated and make use of information theory to quantify how depen- dent two tasks are, e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', using labels entropy as in (Bingel and Søgaard 2017).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' STL-based affinity scores make use of STL models and com- pare them to estimate affinity between tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Common ap- proaches include comparing the STL models latent repre- sentations using e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', the Representation Similarity Analy- sis (Dwivedi and Roig 2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Another option is to compare the STL models attribution maps assuming cooperative tasks use the same features (Caruana 1997).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Also, drawing from Meta-Learning, (Achille et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2019) estimates affinity as the distance between tasks in an embedded space that encodes task complexity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Figure 3: MTL model schematic architecture for two tasks t1 = a and t2 = b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' θB denotes the common backbone weights.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' θHa and θHb denote the separate heads weights.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' MTL-based approaches estimate task affinity during the training of surrogate MTL model(s).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Such computations need to be more efficient than testing all tasks combina- tions, otherwise it would defeat its very purpose of effi- ciently quantifying task affinity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021) proposes an affinity extraction method by simulating the effects that task-specific updates of the model parameters would have on other tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' (Standley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020) extends pairwise MTL performance gain to higher-order task combinations i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', groups of three or more tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Also, both (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021) and (Zhao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018) propose to compute the cosine sim- ilarity between task-specific gradient updates as a way to estimate task affinity during MTL training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 3 Methodology Based on the assumption that grouping cooperative tasks to- gether is a key success factor of MTL, we are interested in quantifying task affinity through several scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In this sec- tion, we motivate the affinity scores selected and we detail the evaluation protocol implemented to benchmark them.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='1 Affinity scores To simplify reasoning on task cooperation and competition, we restrict ourselves to pairwise task affinity estimation, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', affinity scores for 2-task MTL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We depict typical STL and pairwise-MTL architectures in Figures 2 and 3 respectively.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Considering two tasks t1 = a and t2 = b and a batch of examples X, we denote: their resp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' loss functions La and Lb their resp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' STL models STLa and STLb with losses – LST La = La(X, STLa) – LST Lb = Lb(X, STLb) their joint MTL model MTL(a,b) with loss – LMT L(a,b) = La(X, MTL(a,b)) + Lb(X, MTL(a,b)) We consider six task affinity scores that we further describe in the remainder of this section.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Their detailed computations are available in the supplementary material.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Some scores are symmetric, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', assessing how much two tasks a and b help each other regardless of direction;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' others instead are asymmetric, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', assessing how much a target task a bene- fits from being learned with a partner task b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' For each metric we report its category (model-agnostic, STL-based or MTL- based) and contribution (borrowed from literature, revisited from literature or novel).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Input Backbone Head Prediction OB h x 0 HInput Shared backbone Heads s Predictions X Yb 0 HbTaxonomical distance (TD) Model-agnostic – borrowed: A natural way of assessing affinity between tasks from a human perspective is to organize them through a hierarchi- cal taxonomy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' For example, classification datasets such as (Van Horn et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018) or (Wah et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2011) provide hier- archical class granularity that can be used to group similar tasks together as in (Achille et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In our case, we used the tasks similarity tree from (Zamir et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This sym- metric affinity score is computed as the distance between a and b in the tree.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Input attribution similarity (IAS) STL-based – revisited: (Caruana 1997) defines related tasks as tasks that use the same features.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Following this definition we assess how tasks relate to one another in terms of input attribution similarity using InputXGradient (Shrikumar et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2016) to compute attribution maps for STLa and STLb.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The affinity score is then obtained via the cosine similarity of the attribution maps (Song et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Therefore this score is symmetric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Representation similarity analysis (RSA) STL-based – re- visited: RSA, a well-known method in the computational neuro-sciences community (Dwivedi and Roig 2019), relies on the assumption that, if tasks are similar, they learn sim- ilar representations, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', a given input should be projected in similar locations in the latent space.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Referring to fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2, this score compares the latent representations structures be- tween the respective backbones θB of STLa and of STLb.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In a nutshell, RSA uses the Spearman correlation of Repre- sentation Dissimilarity Matrices.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This is a symmetric score.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Label injection (LI) STL-based – novel: Another way to estimate task affinity is to measure the performance gained from adding the target label of another task to the input.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' For example, a task a targeting the classification of handwrit- ten digits could be paired with a task b targeting the predic- tion of even and odd digits.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Since the two tasks are (clearly) related, “injecting” the label of task b, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', providing it as complementary input when training task a, could lead to performance increase for task a.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The performance of label injection can be considered as a proxy of task affinity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This affinity score is asymmetric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' It is computed as the perfor- mance gain between the standard STL model for task a and the b-injected STL model for a denoted by STLa←b, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', LST La − LST La←b LST La←b , (2) using the test losses from the fully trained models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Gradient similarity (GS) MTL-based – borrowed: This task affinity score relies on the assumption that cooperative tasks yield similar i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', non-contradictory, weights updates to the model backbone during MTL training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This score, which we borrow from (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Zhao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018), is sym- metric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' It is computed as the cosine similarity between gra- dients from each task loss with respect to the MTL model common backbone weights.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Using the notation from fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 3, we compute: Scos �∂La(X, θB, θHa) ∂θB , ∂Lb(X, θB, θHb) ∂θB � , (3) at each epoch, and average these cosine similarities across all training epochs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Gradient transference (GT) MTL-based – borrowed: Dur- ing MTL training, by simulating task-specific updates to the common backbone, one can estimate how it would impact the other task’s performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This corresponds to the losses look-ahead ratio defined in (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This asym- metric score is computed comparing the loss of task a af- ter updating the common backbone according to b, and the loss of task a before this simulated update.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Referring to the notation from fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 3, we denote the b-specific update of the common backbone by θB|b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Thus, we compute: La(X, θB|b, θHa) La(X, θB, θHa) , (4) at each epoch, and average these ratios throughout training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='2 Evaluation We evaluate these affinity scores against the true MTL per- formance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Moreover, we evaluate the scores across three lev- els by progressively relaxing the constraint of the analysis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' True performance: MTL gain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' As in (Standley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020), we quantify MTL success as the relative gain between STL and MTL performance in terms of test loss.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' MTL gain for a target task a when using a partner task b is defined as: G(a|b) = LST La − LMT L(¯ a,b) LMT L(¯ a,b) , (5) where LST La is the test loss for task a in a STL config- uration, and LMT L(¯ a,b) is the test loss for task a in a MTL configuration using tasks a and b for joint learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Note that the contribution of task b to the MTL loss is not considered when computing the gain, yet is considered during training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We perform an exhaustive search through all possible pairs of tasks to compute the “ground truth” affinities.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' These serve as baseline against which each affinity score is evaluated.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Level 1: predictive power.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' As previously stated, an ideal affinity score should be a proxy of the actual MTL gain: higher/lower score should imply large/small benefit from joint training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This is a stringent requirement, yet easy to quantify by mean of Pearson’s correlation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Specifically, for each target task a and affinity scoring technique, we com- pute the correlation between the MTL gain across all part- ner tasks (the true performance) and the affinity score across the same partners (the proxy of the performance).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' It follows that affinity scoring techniques with correlation values close to −1 (perfect negative correlation) and +1 (perfect positive correlation) have strong predictive power;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' correlation values close to zero imply no predictive power.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Level 2: partners ranking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' To relax the previous require- ment, we define acceptable an affinity score capable to suc- cessfully rank potential partner tasks by decreasing order of MTL gain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' More formally, for a target task a, and a set of partner tasks P, we want an affinity score δ such that: ∀ ti ∈ P, rank( δ(a, ti) ) = rank( G(a|ti) ), (6) To evaluate the agreement between the ranking given by the affinity score and the actual ranking by MTL gain obtained by exhaustive search, we use Kendall’s correlation coeffi- cient (Kendall 1948) that ranges from −1 (opposite rank- ings) to +1 (same rankings).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Level 3: best partner identification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In case only pairs of tasks are considered for MTL, one is essentially interested in finding the best partner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This means that we can further relax the previous constraint and for a target task a, we want an affinity score δ such that: arg max ti∈P δ(a, ti) = arg max ti∈P G(a|ti), (7) To evaluate this, we report the MTL gain obtained when choosing the top partner according to the affinity score and compare it with the maximum MTL gain obtained when choosing the actual best partner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 4 Results In this section we first detail the data and models used to evaluate the proposed affinity scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Then, we present the results of our empirical campaign along the three levels of evaluation previously defined.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='1 Experimental protocol Dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In this work, we select a portion of the Taskon- omy medium-size split.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This constitutes a representative dataset of Computer Vision tasks, composed of labeled in- door scenes from 73 buildings whose list is available in the supplementary material.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The whole dataset amounts to 726,149 input images which represent approximately 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='2 TB including the various labels.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We select the same five tasks as (Standley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021) to conduct our ex- periments, namely: Semantic segmentation (SemSeg) 2D SURF keypoints identification (Keypts) Edges texture detection (Edges) Depth Z-Buffer estimation (Depth) Surface normals estimation (Normal) A detailed description of the tasks can be found in the sup- plementary material from (Zamir et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Models definition.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We build on the work of (Standley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020) to train five STL models for the five aforementioned tasks and ten pairwise MTL models.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Models are variants of the Xception architecture (Chollet 2017), composed of a backbone that learns a latent representation of the input and MTL gain on Trained with SemSeg Keypts Edges Depth Normal Avg.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' SemSeg 11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='81 10.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='22 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='55 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='95 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='41 Keypts 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='70 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='67 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='87 13.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='88 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='78 Edges 22.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='01 +1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='26 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='24 +2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='18 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='70 Depth +18.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='02 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='81 +16.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='69 6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='37 +6.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='13 Normal +50.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='24 +29.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='56 +78.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='05 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='45 +39.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='35 Table 1: True performance: MTL gain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Ground-truth MTL gain for each target task (column) and each partner task (row) e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', the task Edges performs 78.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='05% better than learned alone in STL when trained with Normal as partner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' a head.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In the case of the STL models, the backbone output is forwarded to a single head that produces the final predic- tion, cf.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In the case of the pairwise MTL models, the shared backbone output is forwarded to two disjoint heads, one for each task under consideration by the MTL model, cf.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' fig.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In this work, as well as in (Standley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021), we only consider hard parameter sharing for the MTL backbone.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' While this approach simplifies rea- soning about shared representations and weights updates, it does not incorporate task interference mitigation strategies.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In terms of model capacity, we replicate the Xception17 models design from prior work in (Standley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020), allowing each STL model only half of the capacity i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', number of Multiply-Add operations, of a pairwise MTL model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This constraint is implemented by reducing the num- ber of channels in the CNN blocks composing the backbone.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Therefore, STL and MTL models use the same architec- ture but with varying capacity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Each model is trained for 50 epochs with a decreasing learning rate, selecting the best- performing epoch on the validation set as final model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Fi- nally, hyper-parameters are set to default values from (Stan- dley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020) with no further tuning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='2 Experimental results In the following we report our evaluation based on the methodology described in section 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The detailed values of each affinity score are instead reported in the supplemen- tary material.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' MTL gain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In Table 1, we report the ground truth MTL gain for each pair of tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We reiterate that these results serve as reference for evaluating the affinity scores.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Furthermore, recall that MTL gains are tightly related to the specific train- ing conditions of our experiment i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', the data, models and hyper-parameters used, and they may vary if computed in another setting.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' From this table, we note that some tasks are more helpful than others.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' For example, Normal is a help- ful partner task, but fails to be significantly assisted by any other task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Overall, we find MTL gains to be highly asym- metric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Nonetheless, almost all tasks would benefit from be- ing learned with their best partner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This is in line with the findings of (Standley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Predictive power.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Table 2 shows the Pearson correlation be- tween the MTL gain and each individual affinity score.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Each Model agnostic STL-based MTL-based Task TD IAS RSA LI GS GT SemSeg 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='99 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='81 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='99 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='79 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='76 Keypts 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='03 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='06 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='37 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='95 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='22 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='08 Edges 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='34 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='44 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='68 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='90 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='37 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='66 Depth 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='90 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='98 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='96 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='64 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='69 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='97 Normal 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='60 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='38 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='20 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='11 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='19 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='40 All-at-once 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='08 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='08 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='15 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='47 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='08 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='02 Table 2: Level 1: predictive power.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Affinity scores correla- tion with MTL gain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', using Label injection (LI) to esti- mate affinities for the target task SemSeg, its output strongly correlates with the actual MTL gains (Pearson corr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='99).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' row considers a separate target task, while the last row la- beled as all-at-once reports the correlation computed using all pairs of all target tasks together.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Starting from such an aggregate scenario, we can see that no scoring technique strongly correlates with the MTL gains.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Only Label injection moderately correlates with MTL gain across all tasks pairs (Pearson corr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='47).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This inval- idates the predictability property desired for an ideal affinity score.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Interestingly, when considering a single target task at a time, some affinity scores successfully predict MTL per- formance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' For example, Depth’s MTL gains can be predicted using Input attribution similarity (corr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='98).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Yet, no scoring provides a stable correlation across all tasks pairs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Partners ranking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In Table 3, we evaluate each affinity scoring technique in terms of its ability to correctly rank po- tential partner tasks according to the MTL gains they pro- vide.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' For a given target task, we compare the rank obtained from the affinity score with the rank obtained from the MTL gains by mean of the Kendall rank correlation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' As in the pre- dictive power evaluation table, each row reports on the cor- relation for each target task separately while in this case the last line summarizes the overall performance using the aver- age of the rank correlations across target tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Starting from the aggregate view, we observe that no score-based ranking correlates strongly with true ranking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Only Label injection and Gradients similarity show a mod- erate and positive correlation (average Kendall corr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' = 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='47 and 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='4 resp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=').' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Differently from before, when considering specific targets tasks, the correlation does not necessarily improve.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' For instance, Keypts and Normal STL-based scores completely fail, yet MTL-based scores are not necessarily better.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Still considering Keypts target task, notice how La- bel injection shows significantly higher Pearson correlation, while the Kendall correlation shows that half of the partner tasks are wrongly ranked according to the affinity score.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Best partner identification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Table 4 shows the top-1 part- ner according to each affinity scoring technique.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This is to be compared with the maximum MTL gain that can be achieved using the actual best partner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Label Injection correctly iden- tifies the best partner for four out of five tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' However, not a single affinity score is capable of correctly identifying Nor- mal’s best partner for MTL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Furthermore, Keypts and Edges Model agnostic STL-based MTL-based Task TD IAS RSA LI GS GT SemSeg 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='33 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='67 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='67 Keypts 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='67 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='33 Edges 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='33 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='33 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='33 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='67 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='33 Depth 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='67 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='67 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='67 Normal 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='33 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='33 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 Average 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='27 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='2 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='47 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='4 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='27 Table 3: Level 2: partners ranking.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Comparison of partner tasks ranking by affinity score versus by MTL gain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', Label injection (LI) perfectly ranks partners for the target task SemSeg (Kendall corr.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='= 1).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' seem to be particularly difficult tasks for best partner identi- fication.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' All scores but Label injection recommend choosing either one as best partner for the other, while the actual best choice is Normal for both of them.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 5 Discussion and future work A perfect affinity score should be both predictive of the ac- tual MTL gain and cheap to compute.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' As prior work hints that the training conditions themselves impact MTL gain, it seems particularly tough to reconcile these properties as we also verify throughout our experimental campaign.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In this paper, we benchmark various affinity scoring techniques that incorporate data, model and hyper-parameters dependencies at varying degrees: from model-agnostic scores that do not take these into account, through STL-based scores that try to include them, to MTL-based scores that are supposed to be the closest to the actual MTL learning conditions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Unfortu- nately, none of the selected scores, not even the MTL-based ones that are close to MTL training, can accurately predict MTL gain across all pairs of tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' However, Label injec- tion, the original affinity score we introduce, appears useful for predicting the gains corresponding to potential partners given a target task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We also observe that, surprisingly, MTL- based scores are not necessarily better than STL-ones, i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', not even quantifying affinity during the actual MTL training seems sufficient to link affinity to performance.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' From a cost perspective, except for Taxonomical dis- tance, all the scoring techniques we benchmark require some model training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We quantify the computational cost of an affinity score by the total amount of training it requires to estimate affinities across all pairs of tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Let us consider n tasks and a standard half-capacity STL model with its re- spective number of Multiply-Add operations denoted by cs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Using this notation, we report the computational cost asso- ciated with each affinity score in Table 5.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' While Input at- tribution similarity and Representation similarity only re- quire one STL model per task2, Label injection requires to train an additional STL-injected model for each ordered pair.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Regarding MTL-based scores, both Gradient similarity and 2In some scenario, the STL models may be readily available, such that the costs associated with Input attribution similarity and Representation similarity can be amortized.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Model-agnostic STL-based MTL-based Task Expected partner TD IAS RSA LI GS GT SemSeg Normal Normal (0) Normal (0) Depth (-32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='2) Normal (0) Depth (-32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='2) Depth (-32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='2) Keypts Normal Edges (-28.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='3) Edges (-28.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='3) Edges (-28.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='3) Normal (0) Edges (-28.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='3) Edges (-28.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='3) Edges Normal Keypts (-86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='7) Keypts (-86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='7) Keypts (-86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='7) Normal (0) Keypts (-86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='7) Keypts (-86.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='7) Depth Normal Normal (0) SemSeg (-0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='1) Normal (0) Normal (0) Normal (0) SemSeg (-0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='1) Normal Edges SemSeg/Depth (-4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='9) SemSeg (-1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='2) Depth (-8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='6) Depth (-8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='6) Depth (-8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='6) Depth (-8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='6) Table 4: Level 3: best partner identification.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Comparison of best partner selection by affinity score.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In parenthesis, we report the difference of MTL gain between the actual best and the selected partner.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='g.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', For the target task Keypts the actual best is Normal and all scores but Label injection (LI) select Edges leading to 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='26 - 29.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='56 = -28.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='3 decrease in performance gain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Model agnostic STL-based MTL-based Cost TD IAS RSA LI GS GT # of Multiply Adds 0 n · cs n · cs n · cs + 2 �n 2 � cs �n 2 � 2cs �n 2 � 2cs Table 5: Affinity scores costs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Comparison of training costs considering all pairs across n tasks, where cs denotes the amount of Multiply-Add operations for a standard half- capacity STL model.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Gradient transference3 require to train a full-capacity MTL model for each unordered pair of tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Finally, while Tax- onomical distance may appear cost-efficient, it has been es- tablished using a Transfer Learning-based taxonomy that it- self requires STL models training, cf.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' (Zamir et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' From a practical perspective, Label injection can correctly identify the best partner for most tasks, except for Normal, for which none of the other scores succeeded.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' As in (Stan- dley et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020), we find that Normal is different from the other tasks: it benefits others but it is better learned alone.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We conjecture that the high complexity of this task makes it a good partner for sharing knowledge during joint learning, but prevents it from being helped by easier tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' To fur- ther corroborate this hypothesis, task complexity need to be incorporated in the affinity scoring design.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We believe that Task2Vec from (Achille et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2019) is a first step towards this direction as it establishes a distance metric between tasks in- corporating task difficulty from a Transfer Learning perspec- tive.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Unfortunately, Task2Vec cannot be directly used in our context as it has only been defined for homogeneous tasks i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', from the same domain.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Indeed, in (Achille et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2019), the tasks are defined using coarse or fine-grained classifica- tion variations from the same hierarchy.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We leave the explo- ration of this research direction as future work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' While this empirical campaign provides a better under- standing of the challenges to take up when designing task affinity scores, it is not conclusive given the high variabil- ity coming from data, models, and tasks used for MTL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In other words, while some results are encouraging, more re- search is required to make those mechanisms actionable for 3We neglect the cost of the simulated task-specific update dur- ing Gradient transference training.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' an actual model design and operation.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In this direction, we identify some limitations that we intend to tackle in future work.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' First, this analysis is limited to five Computer Vision tasks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Some model-agnostic affinity scores such as Taxo- nomical distance might not be trivially adapted to other task domains.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Second, the affinity scores we defined can only estimate pairwise task affinity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' While this is a reasonable starting point, various effects may be at play when learn- ing more than two tasks simultaneously.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' (Zhang, Hayes, and Kanan 2021) propose a new perspective on a sample-wise basis to quantify task transfer and interference separately.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' However, their metrics are defined for classification tasks only and their adaptation to heterogeneous tasks is an open question.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Third, the MTL architecture we selected features hard parameter sharing and a static combined loss with equal weights.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Although this design choice is consistent with prior work and facilitates reasoning on tasks cooperation, it does not take advantage of the recent advances in task interfer- ence mitigation techniques for MTL training (Chen et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Yu et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Kendall, Gal, and Cipolla 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In- deed, tasks may be affine but still interfere during joint learn- ing if no mechanism is implemented to attenuate it, which is why MTL architecture design and task grouping strategies are complementary lines of research.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 6 Conclusion Based on the assumption that identifying cooperative tasks to be learned together is a key success factor of MTL, we borrowed, adapted and designed various task affinity scores for this purpose.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' We benchmarked these scores for pairs of tasks on a public Computer Vision dataset to discuss their strengths and weaknesses.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Although no score is perfectly predictive of MTL gain, some of them still hold value for practitioners, by being able to identify the best partner for a given target task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' This empirical campaign offers a better understanding of the conditions that allow MTL to be supe- rior to STL and sheds light on the challenges to be met when predicting it.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' References Achille, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Lam, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Tewari, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Ravichandran, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Maji, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Fowlkes, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Soatto, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Perona, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2019.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Task2vec: Task embedding for meta-learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In Proceed- ings of the IEEE/CVF international conference on computer vision, 6430–6439.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Bingel, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Søgaard, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Identifying beneficial task relations for multi-task learning in deep neural networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' arXiv preprint arXiv:1702.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='08303.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Caruana, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 1997.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Multitask learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Machine learning, 28(1): 41–75.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Chen, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Badrinarayanan, V.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Lee, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='-Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' ;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Rabinovich, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In International conference on machine learning, 794–803.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' PMLR.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Chollet, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2017.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Xception: Deep learning with depthwise separable convolutions.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In Proceedings of the IEEE con- ference on computer vision and pattern recognition, 1251– 1258.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Dwivedi, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Roig, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2019.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Representation similarity analysis for efficient task taxonomy & transfer learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, 12387–12396.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Fifty, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Amid, E.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Zhao, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Yu, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Anil, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Finn, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Efficiently identifying task groupings for multi- task learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Advances in Neural Information Processing Systems, 34: 27503–27516.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Kendall, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Gal, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Cipolla, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Multi-task learn- ing using uncertainty to weigh losses for scene geometry and semantics.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In Proceedings of the IEEE conference on com- puter vision and pattern recognition, 7482–7491.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Kendall, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 1948.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Rank correlation methods.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Leang, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Sistu, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' B¨urger, F.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Bursuc, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Yogamani, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Dynamic task weighting methods for multi-task net- works in autonomous driving systems.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In 2020 IEEE 23rd International Conference on Intelligent Transportation Sys- tems (ITSC), 1–8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' IEEE.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Liu, Z.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Luo, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Wang, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Tang, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Large- scale celebfaces attributes (celeba) dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Retrieved Au- gust, 15(2018): 11.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Misra, I.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Shrivastava, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Gupta, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Hebert, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2016.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Cross-stitch networks for multi-task learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In Proceed- ings of the IEEE conference on computer vision and pattern recognition, 3994–4003.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Pascal, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Michiardi, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Bost, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Huet, B.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Zuluaga, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Maximum Roaming Multi-Task Learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Pro- ceedings of the AAAI Conference on Artificial Intelligence, 35(10): 9331–9341.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Shrikumar, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Greenside, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Shcherbina, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Kundaje, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2016.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Not just a black box: Learning important features through propagating activation differences.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' arXiv preprint arXiv:1605.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='01713.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Song, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Chen, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Wang, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Shen, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Song, M.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2019.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Deep model transferability from attribution maps.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Advances in Neural Information Processing Systems, 32.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Standley, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Zamir, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Chen, D.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Guibas, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Malik, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Savarese, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Which tasks should be learned together in multi-task learning?' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In International Conference on Ma- chine Learning, 9120–9132.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' PMLR.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Sun, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Panda, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Feris, R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Saenko, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Adashare: Learning what to share for efficient deep multi- task learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Advances in Neural Information Processing Systems, 33: 8728–8740.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Van Horn, G.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Mac Aodha, O.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Song, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Cui, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Sun, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Shepard, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Adam, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Perona, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Belongie, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The inaturalist species classification and detection dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In Proceedings of the IEEE conference on computer vision and pattern recognition, 8769–8778.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Wah, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Branson, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Welinder, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Perona, P.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Belongie, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2011.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' The caltech-ucsd birds-200-2011 dataset.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Yu, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Kumar, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Gupta, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Levine, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Hausman, K.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Finn, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2020.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Gradient surgery for multi-task learn- ing.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Advances in Neural Information Processing Systems, 33: 5824–5836.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Zamir, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' R.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Sax, A.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Shen, W.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Guibas, L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Malik, J.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Savarese, S.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Taskonomy: Disentangling task transfer learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In Proceedings of the IEEE conference on com- puter vision and pattern recognition, 3712–3722.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Zhang, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Hayes, T.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' L.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Kanan, C.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Disentangling Transfer and Interference in Multi-Domain Learning.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' arXiv preprint arXiv:2107.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='05445.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Zhao, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Li, H.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Shen, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Liang, X.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=';' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' and Wu, Y.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' A modulation module for multi-task learning with applications in image retrieval.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' In Proceedings of the European Confer- ence on Computer Vision (ECCV), 401–416.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Abstract The following elements are provided in the supplementary material: 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Affinity scores raw values 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Taskonomy buildings used 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Affinity scores computation A Affinity scores raw values In Tables 6 to 11, we report the raw affinities estimations for all tasks, using each affinity scoring technique.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Results are rounded at the second decimal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Affinities estimations with SemSeg Keypts Edges Depth Normal SemSeg 8 6 8 5 Keypts 8 4 12 9 Edges 6 4 10 7 Depth 8 12 10 5 Normal 5 9 7 5 Table 6: Taxonomical distance (TD).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Distance between tasks in the similarity tree from (Zamir et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Multiplied by −1 for consistency (i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', higher means more affinity).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Affinities estimations with SemSeg Keypts Edges Depth Normal SemSeg 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='25 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='23 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='31 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='45 Keypts 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='25 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='52 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='18 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='23 Edges 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='23 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='52 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='18 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='22 Depth 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='31 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='18 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='18 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='29 Normal 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='45 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='23 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='22 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='29 Table 7: Input attribution similarity (IAS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Cosine similarity between STL models attribution maps.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Affinities estimations with SemSeg Keypts Edges Depth Normal SemSeg 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='33 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='37 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='46 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='46 Keypts 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='33 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='66 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='04 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='05 Edges 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='37 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='66 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='12 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='13 Depth 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='46 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='04 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='12 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='69 Normal 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='46 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='05 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='13 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='69 Table 8: Representation similarity analysis (RSA).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Represen- tation similarity analysis using the STL models backbones output.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Affinities estimations with SemSeg Keypts Edges Depth Normal SemSeg 2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='93 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='50 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='07 +3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='70 Keypts 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='47 +4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='97 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='31 +1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='42 Edges 15.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='93 4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='20 9.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='58 +2.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='42 Depth +4.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='04 3.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='34 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='26 +20.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='29 Normal +25.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='68 +60.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='29 +23.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='79 +66.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='30 Table 9: Label injection (LI).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Performance gain (%) when incorporating the label from the partner task in the STL model’s input, relative to standard STL.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Affinities estimations with SemSeg Keypts Edges Depth Normal SemSeg 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='51 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='39 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='93 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='54 Keypts 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='51 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='89 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='75 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 Edges 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='39 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='89 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='92 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='59 Depth 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='93 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='75 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='92 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='40 Normal 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='54 1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='0 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='59 8.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='40 Table 10: Gradient similarity (GS).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Cosine similarity be- tween task-specific gradient updates on the MTL backbone.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Averaged across all training epochs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Multiplied by 100.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Affinities estimations with SemSeg Keypts Edges Depth Normal SemSeg +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='02 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='25 +1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='69 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='74 Keypts 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='03 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='38 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='01 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='01 Edges 0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='20 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='71 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='19 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='27 Depth +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='47 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='01 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='15 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='90 Normal +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='27 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='03 +0.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='16 +1.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='26 Table 11: Gradient transference (GT).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Look-ahead ratio sim- ulating the effect of applying task-specific updates to the MTL backbone for the other task.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Averaged across all train- ing epochs.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' B Taskonomy buildings used We split our subset of the Taskonomy dataset into train, val- idation and test sets, on a per-building basis.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Train set These buildings amount to 603,437 input images.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' ' metadata={'source': 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' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='forkland ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='mifflinburg ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='ranchester ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='springerville ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='swisshome ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='westfield ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='willow ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='winooski ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='hainesburg ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='irvine ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='pearce ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='thrall ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='tilghmanton ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='uvalda ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='sugarville ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='silas ' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='Validation set These buildings amount to 82,' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='345 input images.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' corozal collierville markleeville darden chilhowie churchton cauthron cousins timberon wando Test set These buildings amount to 40,367 input images.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' ihlen muleshoe noxapater mcdade C Affinity scores computation In Table 12, we detail the computation of each selected affin- ity score.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Considering two tasks t1 = a and t2 = b and a batch of examples X, we denote: their resp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' losses functions La and Lb their resp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' STL models STLa and STLb with losses – LST La = La(X, STLa) – LST Lb = Lb(X, STLb) their joint MTL model MTL(a,b) with loss LMT L(a,b) = La(X, MTL(a,b)) + Lb(X, MTL(a,b)) Note that if the score is symmetric, it assesses how much the two tasks help each other regardless of direction.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' If it is asymmetric, it considers how much the target task a benefits from being learned with the partner task b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' While all scores could not be constrained to lie in the same range, higher al- ways means more affinity.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Affinity scoring Type Computation Comment Range Taxonomical distance (TD) Model- agnostic Distance between tasks in a taxonomy tree.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Symmetric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Taxonomy borrowed from (Zamir et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Multiplied by −1 for consistency i.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content='e.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=', higher is better.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' ] − ∞, 0] Input attribution similarity (IAS) STL- based 1 |X| � x∈X Scos(Attr(STLa, x), Attr(STLb, x)), (8) where Scos is cosine similarity, X denotes a batch of examples and Attr the attribution method used.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Symmetric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Revisited from (Song et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Computed on a subset of the test set (2,048 images) using InputXGradient attribution (Shriku- mar et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2016).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' [−1, +1] Representation similarity analysis (RSA) STL- based RSA(θBa, θBb, X), (9) where RSA denotes the Representation Similiarity Anal- ysis, X a batch of examples, θBa and θBb the back- bone weights of the STL models for tasks a and b resp.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Symmetric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Revisited from (Dwivedi and Roig 2019).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Computed on a subset of the test set (2,048 images).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' [−1, +1] Label injection (LI) STL- based LST La − LST La←b LST La←b , (10) where STLa←b represents the STL model for task a, modified to ingest the correspond- ing label from task b in addition to the input.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Asymmetric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Novel proposal.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Computed using test losses.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' ]−∞, +∞[ Gradient similarity (GS) MTL- based 1 N N � i=1 Scos(∂La(X, θi B, θi Ha) ∂θi B , ∂Lb(X, θi B, θi Hb) ∂θi B ), (11) where N denotes the number of training epochs, Scos the cosine similarity, X a batch of examples, θi B the weights of the common MTL backbone at the ith epoch, θi Ha and θi Hb the weights of the heads for a and b at the ith epoch.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Symmetric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Borrowed from (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021;' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Zhao et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2018).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' [−1, +1] Gradient transference (GT) MTL- based 1 N N � i=1 1 − La(X, θi+1 B|b, θi Ha) La(X, θi B, θi Ha) , (12) where N denotes the number of training epochs, X a batch of examples, θi+1 B|b the weights of the common MTL backbone updated using the loss of task b at the epoch i + 1, θi Ha and θi Hb the weights of the heads for a and b at the ith epoch.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Asymmetric.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' Borrowed from (Fifty et al.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' 2021).' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'} +page_content=' ]−∞, +∞[ Table 12: Tasks affinity scores description and computation considering two tasks t1 = a and t2 = b.' metadata={'source': '/home/zjlab/wf/langchain-ChatGLM/knowledge_base/ANE1T4oBgHgl3EQfDQMU/content/2301.02873v1.pdf'}