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Conditional Positional Encodings for Vision Transformers | https://openreview.net/forum?id=3KWnuT-R1bh | https://openreview.net/forum?id=3KWnuT-R1bh | Xiangxiang Chu,Zhi Tian,Bo Zhang,Xinlong Wang,Chunhua Shen | ICLR 2023,Poster | We propose a conditional positional encoding (CPE) scheme for vision Transformers. Unlike previous fixed or learnable positional encodings that are predefined and independent of input tokens, CPE is dynamically generated and conditioned on the local neighborhood of the input tokens. As a result, CPE can easily generalize to the input sequences that are longer than what the model has ever seen during the training. Besides, CPE can keep the desired translation equivalence in vision tasks, resulting in improved performance. We implement CPE with a simple Position Encoding Generator (PEG) to get seamlessly incorporated into the current Transformer framework. Built on PEG, we present Conditional Position encoding Vision Transformer (CPVT). We demonstrate that CPVT has visually similar attention maps compared to those with learned positional encodings and delivers outperforming results. | https://openreview.net/pdf/980d5a9d543186a53b4957b0f7b63592a3965b2e.pdf |
ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills | https://openreview.net/forum?id=b_CQDy9vrD1 | https://openreview.net/forum?id=b_CQDy9vrD1 | Jiayuan Gu,Fanbo Xiang,Xuanlin Li,Zhan Ling,Xiqiang Liu,Tongzhou Mu,Yihe Tang,Stone Tao,Xinyue Wei,Yunchao Yao,Xiaodi Yuan,Pengwei Xie,Zhiao Huang,Rui Chen,Hao Su | ICLR 2023,Poster | Generalizable manipulation skills, which can be composed to tackle long-horizon and complex daily chores, are one of the cornerstones of Embodied AI. However, existing benchmarks, mostly composed of a suite of simulatable environments, are insufficient to push cutting-edge research works because they lack object-level topological and geometric variations, are not based on fully dynamic simulation, or are short of native support for multiple types of manipulation tasks. To this end, we present ManiSkill2, the next generation of the SAPIEN ManiSkill benchmark, to address critical pain points often encountered by researchers when using benchmarks for generalizable manipulation skills. ManiSkill2 includes 20 manipulation task families with 2000+ object models and 4M+ demonstration frames, which cover stationary/mobile-base, single/dual-arm, and rigid/soft-body manipulation tasks with 2D/3D-input data simulated by fully dynamic engines. It defines a unified interface and evaluation protocol to support a wide range of algorithms (e.g., classic sense-plan-act, RL, IL), visual observations (point cloud, RGBD), and controllers (e.g., action type and parameterization). Moreover, it empowers fast visual input learning algorithms so that a CNN-based policy can collect samples at about 2000 FPS with 1 GPU and 16 processes on a regular workstation. It implements a render server infrastructure to allow sharing rendering resources across all environments, thereby significantly reducing memory usage. We open-source all codes of our benchmark (simulator, environments, and baselines) and host an online challenge open to interdisciplinary researchers. | https://openreview.net/pdf/a180ff20bf660bc9f8a2f007193ed71e83ba91f7.pdf |
Deja Vu: Continual Model Generalization for Unseen Domains | https://openreview.net/forum?id=L8iZdgeKmI6 | https://openreview.net/forum?id=L8iZdgeKmI6 | Chenxi Liu,Lixu Wang,Lingjuan Lyu,Chen Sun,Xiao Wang,Qi Zhu | ICLR 2023,Poster | In real-world applications, deep learning models often run in non-stationary environments where the target data distribution continually shifts over time. There have been numerous domain adaptation (DA) methods in both online and offline modes to improve cross-domain adaptation ability. However, these DA methods typically only provide good performance after a long period of adaptation, and perform poorly on new domains before and during adaptation – in what we call the “Unfamiliar Period”, especially when domain shifts happen suddenly and significantly. On the other hand, domain generalization (DG) methods have been proposed to improve the model generalization ability on unadapted domains. However, existing DG works are ineffective for continually changing domains due to severe catastrophic forgetting of learned knowledge. To overcome these limitations of DA and DG in handling the Unfamiliar Period during continual domain shift, we propose RaTP, a framework that focuses on improving models’ target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains. RaTP includes a training-free data augmentation module to prepare data for TDG, a novel pseudo-labeling mechanism to provide reliable supervision for TDA, and a prototype contrastive alignment algorithm to align different domains for achieving TDG, TDA and FA. Extensive experiments on Digits, PACS, and DomainNet demonstrate that RaTP significantly outperforms state-of-the-art works from Continual DA, Source-Free DA, Test-Time/Online DA, Single DG, Multiple DG and Unified DA&DG in TDG, and achieves comparable TDA and FA capabilities. | https://openreview.net/pdf/308a0282513449f22438eeb7ca60ec1b03b7f924.pdf |
A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps | https://openreview.net/forum?id=65XDF_nwI61 | https://openreview.net/forum?id=65XDF_nwI61 | Kiarash Jamali,Dari Kimanius,Sjors HW Scheres | ICLR 2023,Poster | Electron cryo-microscopy (cryo-EM) produces three-dimensional (3D) maps of the electrostatic potential of biological macromolecules, including proteins. At sufficient resolution, the cryo-EM maps, along with some knowledge about the imaged molecules, allow de novo atomic modelling. Typically, this is done through a laborious manual process. Recent advances in machine learning applications to protein structure prediction show potential for automating this process. Taking inspiration from these techniques, we have built ModelAngelo for automated model building of proteins in cryo-EM maps. ModelAngelo first uses a residual convolutional neural network (CNN) to initialize a graph representation with nodes assigned to individual amino acids of the proteins in the map and edges representing the protein chain. The graph is then refined with a graph neural network (GNN) that combines the cryo-EM data, the amino acid sequence data and prior knowledge about protein geometries. The GNN refines the geometry of the protein chain and classifies the amino acids for each of its nodes. The final graph is post-processed with a hidden Markov model (HMM) search to map each protein chain to entries in a user provided sequence file. Application to 28 test cases shows that ModelAngelo outperforms state-of-the-art and approximates manual building for cryo-EM maps with resolutions better than 3.5 A. | https://openreview.net/pdf/bdb1a16f91aaf86c92ff8410163dc3ed726877bc.pdf |
Distilling Cognitive Backdoor Patterns within an Image | https://openreview.net/forum?id=S3D9NLzjnQ5 | https://openreview.net/forum?id=S3D9NLzjnQ5 | Hanxun Huang,Xingjun Ma,Sarah Monazam Erfani,James Bailey | ICLR 2023,Poster | This paper proposes a simple method to distill and detect backdoor patterns within an image: \emph{Cognitive Distillation} (CD). The idea is to extract the ``minimal essence" from an input image responsible for the model's prediction. CD optimizes an input mask to extract a small pattern from the input image that can lead to the same model output (i.e., logits or deep features). The extracted pattern can help understand the cognitive mechanism of a model on clean vs. backdoor images and is thus called a \emph{Cognitive Pattern} (CP). Using CD and the distilled CPs, we uncover an interesting phenomenon of backdoor attacks: despite the various forms and sizes of trigger patterns used by different attacks, the CPs of backdoor samples are all surprisingly and suspiciously small.
One thus can leverage the learned mask to detect and remove backdoor examples from poisoned training datasets.
We conduct extensive experiments to show that CD can robustly detect a wide range of advanced backdoor attacks.
We also show that CD can potentially be applied to help detect potential biases from face datasets.
Code is available at https://github.com/HanxunH/CognitiveDistillation. | https://openreview.net/pdf/9582391717db932771feaf1c877a1ff5a58478f5.pdf |
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching | https://openreview.net/forum?id=QjQibO3scV_ | https://openreview.net/forum?id=QjQibO3scV_ | Chang Liu,Zetian Jiang,Runzhong Wang,Lingxiao Huang,Pinyan Lu,Junchi Yan | ICLR 2023,Poster | Graph matching (GM) has been a building block in various areas including computer vision and pattern recognition. Despite recent impressive progress, existing deep GM methods often have obvious difficulty in handling outliers, which are ubiquitous in practice. We propose a deep reinforcement learning based approach RGM, whose sequential node matching scheme naturally fits the strategy for selective inlier matching against outliers. A revocable action framework is devised to improve the agent's flexibility against the complex constrained GM. Moreover, we propose a quadratic approximation technique to regularize the affinity score, in the presence of outliers. As such, the agent can finish inlier matching timely when the affinity score stops growing, for which otherwise an additional parameter i.e. the number of inliers is needed to avoid matching outliers. In this paper, we focus on learning the back-end solver under the most general form of GM: the Lawler's QAP, whose input is the affinity matrix. Especially, our approach can also boost existing GM methods that use such input. Experiments on multiple real-world datasets demonstrate its performance regarding both accuracy and robustness. | https://openreview.net/pdf/11e4101c1705eaa7c0f4626439b493d3b84ed36e.pdf |
One Transformer Can Understand Both 2D & 3D Molecular Data | https://openreview.net/forum?id=vZTp1oPV3PC | https://openreview.net/forum?id=vZTp1oPV3PC | Shengjie Luo,Tianlang Chen,Yixian Xu,Shuxin Zheng,Tie-Yan Liu,Liwei Wang,Di He | ICLR 2023,Poster | Unlike vision and language data which usually has a unique format, molecules can naturally be characterized using different chemical formulations. One can view a molecule as a 2D graph or define it as a collection of atoms located in a 3D space. For molecular representation learning, most previous works designed neural networks only for a particular data format, making the learned models likely to fail for other data formats. We believe a general-purpose neural network model for chemistry should be able to handle molecular tasks across data modalities. To achieve this goal, in this work, we develop a novel Transformer-based Molecular model called Transformer-M, which can take molecular data of 2D or 3D formats as input and generate meaningful semantic representations. Using the standard Transformer as the backbone architecture, Transformer-M develops two separated channels to encode 2D and 3D structural information and incorporate them with the atom features in the network modules. When the input data is in a particular format, the corresponding channel will be activated, and the other will be disabled. By training on 2D and 3D molecular data with properly designed supervised signals, Transformer-M automatically learns to leverage knowledge from different data modalities and correctly capture the representations. We conducted extensive experiments for Transformer-M. All empirical results show that Transformer-M can simultaneously achieve strong performance on 2D and 3D tasks, suggesting its broad applicability. The code and models will be made publicly available at https://github.com/lsj2408/Transformer-M. | https://openreview.net/pdf/1f1e2fe5093c5069af104d34dca2fbf3d580a644.pdf |
Mind the Gap: Offline Policy Optimization for Imperfect Rewards | https://openreview.net/forum?id=WumysvcMvV6 | https://openreview.net/forum?id=WumysvcMvV6 | Jianxiong Li,Xiao Hu,Haoran Xu,Jingjing Liu,Xianyuan Zhan,Qing-Shan Jia,Ya-Qin Zhang | ICLR 2023,Poster | Reward function is essential in reinforcement learning (RL), serving as the guiding signal to incentivize agents to solve given tasks, however, is also notoriously difficult to design. In many cases, only imperfect rewards are available, which inflicts substantial performance loss for RL agents. In this study, we propose a unified offline policy optimization approach, \textit{RGM (Reward Gap Minimization)}, which can smartly handle diverse types of imperfect rewards. RGM is formulated as a bi-level optimization problem: the upper layer optimizes a reward correction term that performs visitation distribution matching w.r.t. some expert data; the lower layer solves a pessimistic RL problem with the corrected rewards. By exploiting the duality of the lower layer, we derive a tractable algorithm that enables sampled-based learning without any online interactions. Comprehensive experiments demonstrate that RGM achieves superior performance to existing methods under diverse settings of imperfect rewards. Further, RGM can effectively correct wrong or inconsistent rewards against expert preference and retrieve useful information from biased rewards. Code is available at https://github.com/Facebear-ljx/RGM. | https://openreview.net/pdf/294d37fbe5e33afb6db7a747e408f4ff688a1f9a.pdf |
Learning to Compose Soft Prompts for Compositional Zero-Shot Learning | https://openreview.net/forum?id=S8-A2FXnIh | https://openreview.net/forum?id=S8-A2FXnIh | Nihal V. Nayak,Peilin Yu,Stephen Bach | ICLR 2023,Poster | We introduce compositional soft prompting (CSP), a parameter-efficient learning technique to improve the zero-shot compositionality of large-scale pretrained vision-language models (VLMs) like CLIP. We develop CSP for compositional zero-shot learning, the task of predicting unseen attribute-object compositions (e.g., old cat and young tiger). VLMs have a flexible text encoder that can represent arbitrary classes as natural language prompts but they often underperform task-specific architectures on the compositional zero-shot benchmark datasets. CSP treats the attributes and objects that define classes as learnable tokens of vocabulary. During training, the vocabulary is tuned to recognize classes that compose tokens in multiple ways (e.g., old cat and white cat). At test time, we recompose the learned attribute-object vocabulary in new combinations to recognize novel classes. We show that CSP outperforms the CLIP on benchmark datasets by an average of 10.9 percentage points on AUC. CSP also outperforms CoOp, a soft prompting method that fine-tunes the prefix context tokens, by an average of 5.8 percentage points on AUC. We perform additional experiments to show that CSP improves generalization to higher-order attribute-attribute-object compositions (e.g., old white cat) and combinations of pretrained attributes and fine-tuned objects. The code is available at https://github.com/BatsResearch/csp. | https://openreview.net/pdf/0e212bc778de607b8b27f2b040f6b63d6261d59a.pdf |
SQA3D: Situated Question Answering in 3D Scenes | https://openreview.net/forum?id=IDJx97BC38 | https://openreview.net/forum?id=IDJx97BC38 | Xiaojian Ma,Silong Yong,Zilong Zheng,Qing Li,Yitao Liang,Song-Chun Zhu,Siyuan Huang | ICLR 2023,Poster | We propose a new task to benchmark scene understanding of embodied agents: Situated Question Answering in 3D Scenes (SQA3D). Given a scene context (e.g., 3D scan), SQA3D requires the tested agent to first understand its situation (position, orientation, etc.) in the 3D scene as described by text, then reason about its surrounding environment and answer a question under that situation. Based upon 650 scenes from ScanNet, we provide a dataset centered around 6.8k unique situations, along with 20.4k descriptions and 33.4k diverse reasoning questions for these situations. These questions examine a wide spectrum of reasoning capabilities for an intelligent agent, ranging from spatial relation comprehension to commonsense understanding, navigation, and multi-hop reasoning. SQA3D imposes a significant challenge to current multi-modal especially 3D reasoning models. We evaluate various state-of-the-art approaches and find that the best one only achieves an overall score of 47.20%, while amateur human participants can reach 90.06%. We believe SQA3D could facilitate future embodied AI research with stronger situation understanding and reasoning capability. | https://openreview.net/pdf/7c0e4e7d24c7883389f21b015135c6bfd4f50b62.pdf |
Empowering Networks With Scale and Rotation Equivariance Using A Similarity Convolution | https://openreview.net/forum?id=NJENsJ37sQ | https://openreview.net/forum?id=NJENsJ37sQ | Zikai Sun,Thierry Blu | ICLR 2023,Poster | The translational equivariant nature of Convolutional Neural Networks (CNNs) is a reason for its great success in computer vision. However, networks do not enjoy more general equivariance properties such as rotation or scaling, ultimately limiting their generalization performance. To address this limitation, we devise a method that endows CNNs with simultaneous equivariance with respect to translation, rotation, and scaling. Our approach defines a convolution-like operation and ensures equivariance based on our proposed scalable Fourier-Argand representation. The method maintains similar efficiency as a traditional network and hardly introduces any additional learnable parameters, since it does not face the computational issue that often occurs in group-convolution operators. We validate the efficacy of our approach in the image classification task, demonstrating its robustness and the generalization ability to both scaled and rotated inputs. | https://openreview.net/pdf/8d310f20e54ef071f9265cf12cf4959583d516b3.pdf |
Robust and Controllable Object-Centric Learning through Energy-based Models | https://openreview.net/forum?id=wcNtbEtcGIC | https://openreview.net/forum?id=wcNtbEtcGIC | Ruixiang ZHANG,Tong Che,Boris Ivanovic,Renhao Wang,Marco Pavone,Yoshua Bengio,Liam Paull | ICLR 2023,Poster | Humans are remarkably good at understanding and reasoning about complex visual scenes. The capability of decomposing low-level observations into discrete objects allows us to build a grounded abstract representation and identify the compositional structure of the world. Thus it is a crucial step for machine learning models to be capable of inferring objects and their properties from visual scene without explicit supervision. However, existing works on object-centric representation learning are either relying on tailor-made neural network modules or assuming sophisticated models of underlying generative and inference processes. In this work, we present EGO, a conceptually simple and general approach to learning object-centric representation through energy-based model. By forming a permutation-invariant energy function using vanilla attention blocks that are readily available in Transformers, we can infer object-centric latent variables via gradient-based MCMC methods where permutation equivariance is automatically guaranteed. We show that EGO can be easily integrated into existing architectures, and can effectively extract high-quality object-centric representations, leading to better segmentation accuracy and competitive downstream task performance. We empirically evaluate the robustness of the learned representation from EGO against distribution shift. Finally, we demonstrate the effectiveness of EGO in systematic compositional generalization, by recomposing learned energy functions for novel scene generation and manipulation. | https://openreview.net/pdf/e1dc654960581d47d9f3d978a44b2de25897d983.pdf |
Topology-aware Robust Optimization for Out-of-Distribution Generalization | https://openreview.net/forum?id=ylMq8MBnAp | https://openreview.net/forum?id=ylMq8MBnAp | Fengchun Qiao,Xi Peng | ICLR 2023,Poster | Out-of-distribution (OOD) generalization is a challenging machine learning problem yet highly desirable in many high-stake applications.
Existing methods suffer from overly pessimistic modeling with low generalization confidence. As generalizing to arbitrary test distributions is impossible, we hypothesize that further structure on the topology of distributions is crucial in developing strong OOD resilience. To this end, we propose topology-aware robust optimization (TRO) that seamlessly integrates distributional topology in a principled optimization framework. More specifically, TRO solves two optimization objectives: (1) Topology Learning which explores data manifold to uncover the distributional topology; (2) Learning on Topology which exploits the topology to constrain robust optimization for tightly-bounded generalization risks. We theoretically demonstrate the effectiveness of our approach, and empirically show that it significantly outperforms the state of the arts in a wide range of tasks including classification, regression, and semantic segmentation. Moreover, we empirically find the data-driven distributional topology is consistent with domain knowledge, enhancing the explainability of our approach. | https://openreview.net/pdf/059e26bad7131b93661128c88df3bf50bf86f1bd.pdf |
EAGLE: Large-scale Learning of Turbulent Fluid Dynamics with Mesh Transformers | https://openreview.net/forum?id=mfIX4QpsARJ | https://openreview.net/forum?id=mfIX4QpsARJ | Steeven JANNY,Aurélien Bénéteau,Madiha Nadri,Julie Digne,Nicolas THOME,Christian Wolf | ICLR 2023,Poster | Estimating fluid dynamics is classically done through the simulation and integration of numerical models solving the Navier-Stokes equations, which is computationally complex and time-consuming even on high-end hardware. This is a notoriously hard problem to solve, which has recently been addressed with machine learning, in particular graph neural networks (GNN) and variants trained and evaluated on datasets of static objects in static scenes with fixed geometry. We attempt to go beyond existing work in complexity and introduce a new model, method and benchmark. We propose EAGLE: a large-scale dataset of ∼1.1 million 2D meshes resulting from simulations of unsteady fluid dynamics caused by a moving flow source interacting with nonlinear scene structure of varying geometries, with 600 different scenes of three different types in total. To perform future forecasting of pressure and velocity on the challenging EAGLE dataset, we introduce a new mesh transformer. It leverages node clustering, graph pooling and global attention to learn long-range dependencies between spatially distant data points without needing a large number of iterations, as existing GNN methods do. We show that our transformer outperforms state-of-the-art performance on, both, existing synthetic and real datasets and on EAGLE. Finally, we highlight that our approach learns to attend to airflow, integrating complex information in a single iteration. | https://openreview.net/pdf/09aee140cdc4dcf6fcc6f93e1be103f7e4d37584.pdf |
Limitless Stability for Graph Convolutional Networks | https://openreview.net/forum?id=XqcQhVUr2h0 | https://openreview.net/forum?id=XqcQhVUr2h0 | Christian Koke | ICLR 2023,Poster | This work establishes rigorous, novel and widely applicable stability guarantees and transferability bounds for general graph convolutional networks -- without reference to any underlying limit object or statistical distribution. Crucially, utilized graph-shift operators are not necessarily assumed to be normal, allowing for the treatment of networks on both directed- and undirected graphs within the developed framework. In the undirected setting, stability to node-level perturbations is related to an 'adequate spectral covering' property of the filters in each layer. Stability to edge-level perturbations is discussed and related to properties of the utilized filters such as their Lipschitz constants. Results on stability to vertex-set non-preserving perturbations are obtained by utilizing recently developed mathematical-physics based tools. As an exemplifying application of the developed theory, it is showcased that
general graph convolutional networks utilizing the un-normalized graph Laplacian as graph-shift-operator can be rendered stable to collapsing strong edges in the underlying graph if filters are mandated to be constant at infinity. These theoretical results are supported by corresponding numerical investigations showcasing the response of filters and networks to such perturbations. | https://openreview.net/pdf/6dd7112d0d6e4fc556d56e3cc15f8184f052e543.pdf |
De Novo Molecular Generation via Connection-aware Motif Mining | https://openreview.net/forum?id=Q_Jexl8-qDi | https://openreview.net/forum?id=Q_Jexl8-qDi | Zijie Geng,Shufang Xie,Yingce Xia,Lijun Wu,Tao Qin,Jie Wang,Yongdong Zhang,Feng Wu,Tie-Yan Liu | ICLR 2023,Poster | De novo molecular generation is an essential task for science discovery. Recently, fragment-based deep generative models have attracted much research attention due to their flexibility in generating novel molecules based on existing molecule fragments. However, the motif vocabulary, i.e., the collection of frequent fragments, is usually built upon heuristic rules, which brings difficulties to capturing common substructures from large amounts of molecules. In this work, we propose MiCaM to generate molecules based on mined connection-aware motifs. Specifically, it leverages a data-driven algorithm to automatically discover motifs from a molecule library by iteratively merging subgraphs based on their frequency. The obtained motif vocabulary consists of not only molecular motifs (i.e., the frequent fragments), but also their connection information, indicating how the motifs are connected with each other. Based on the mined connection-aware motifs, MiCaM builds a connection-aware generator, which simultaneously picks up motifs and determines how they are connected. We test our method on distribution-learning benchmarks (i.e., generating novel molecules to resemble the distribution of a given training set) and goal-directed benchmarks (i.e., generating molecules with target properties), and achieve significant improvements over previous fragment-based baselines. Furthermore, we demonstrate that our method can effectively mine domain-specific motifs for different tasks. | https://openreview.net/pdf/a9499c0afc7fb88fee1a3e168e3d543e7eb275ea.pdf |
Revisiting the Entropy Semiring for Neural Speech Recognition | https://openreview.net/forum?id=SNgLnzFQeiD | https://openreview.net/forum?id=SNgLnzFQeiD | Oscar Chang,Dongseong Hwang,Olivier Siohan | ICLR 2023,Poster | In streaming settings, speech recognition models have to map sub-sequences of speech to text before the full audio stream becomes available. However, since alignment information between speech and text is rarely available during training, models need to learn it in a completely self-supervised way. In practice, the exponential number of possible alignments makes this extremely challenging, with models often learning peaky or sub-optimal alignments. Prima facie, the exponential nature of the alignment space makes it difficult to even quantify the uncertainty of a model's alignment distribution. Fortunately, it has been known for decades that the entropy of a probabilistic finite state transducer can be computed in time linear to the size of the transducer via a dynamic programming reduction based on semirings. In this work, we revisit the entropy semiring for neural speech recognition models, and show how alignment entropy can be used to supervise models through regularization or distillation. We also contribute an open-source implementation of CTC and RNN-T in the semiring framework that includes numerically stable and highly parallel variants of the entropy semiring. Empirically, we observe that the addition of alignment distillation improves the accuracy and latency of an already well-optimized teacher-student distillation model, achieving state-of-the-art performance on the Librispeech dataset in the streaming scenario. | https://openreview.net/pdf/b7e99fa5c4b5a398924bd8648c2768fc6925c80e.pdf |
Rethinking skip connection model as a learnable Markov chain | https://openreview.net/forum?id=yQdBtFfleh6 | https://openreview.net/forum?id=yQdBtFfleh6 | Dengsheng Chen,Jie Hu,Wenwen Qiang,Xiaoming Wei,Enhua Wu | ICLR 2023,Poster | Over the past few years afterward the birth of ResNet, skip connection has become the defacto standard for the design of modern architectures due to its widespread adoption, easy optimization, and proven performance.
Prior work has explained the effectiveness of the skip connection mechanism from different perspectives.
In this work, we deep dive into the model's behaviors with skip connections which can be formulated as a learnable Markov chain.
An efficient Markov chain is preferred as it always maps the input data to the target domain in a better way.
However, while a model is explained as a Markov chain, it is not guaranteed to be optimized following an efficient Markov chain by existing SGD-based optimizers prone to getting trapped in local optimal points.
In order to move towards a more efficient Markov chain, we propose a simple routine of penal connection to make any residual-like model become a learnable Markov chain.
Aside from that, the penal connection can also be viewed as a particular model regularization and can be easily implemented with one line of code in the most popular deep learning frameworks.
The encouraging experimental results in multi-modal translation and image recognition empirically confirm our conjecture of the learnable Markov chain view and demonstrate the superiority of the proposed penal connection. | https://openreview.net/pdf/2ffda559a1197d97213255e139462d4270bfe2cf.pdf |
Measuring axiomatic soundness of counterfactual image models | https://openreview.net/forum?id=lZOUQQvwI3q | https://openreview.net/forum?id=lZOUQQvwI3q | Miguel Monteiro,Fabio De Sousa Ribeiro,Nick Pawlowski,Daniel C. Castro,Ben Glocker | ICLR 2023,Poster | We present a general framework for evaluating image counterfactuals. The power and flexibility of deep generative models make them valuable tools for learning mechanisms in structural causal models. However, their flexibility makes counterfactual identifiability impossible in the general case.
Motivated by these issues, we revisit Pearl's axiomatic definition of counterfactuals to determine the necessary constraints of any counterfactual inference model: composition, reversibility, and effectiveness. We frame counterfactuals as functions of an input variable, its parents, and counterfactual parents and use the axiomatic constraints to restrict the set of functions that could represent the counterfactual, thus deriving distance metrics between the approximate and ideal functions. We demonstrate how these metrics can be used to compare and choose between different approximate counterfactual inference models and to provide insight into a model's shortcomings and trade-offs. | https://openreview.net/pdf/612f4ebbac94eb2919e2ce21756885ab089b5461.pdf |
Alternating Differentiation for Optimization Layers | https://openreview.net/forum?id=KKBMz-EL4tD | https://openreview.net/forum?id=KKBMz-EL4tD | Haixiang Sun,Ye Shi,Jingya Wang,Hoang Duong Tuan,H. Vincent Poor,Dacheng Tao | ICLR 2023,Poster | The idea of embedding optimization problems into deep neural networks as optimization layers to encode constraints and inductive priors has taken hold in recent years. Most existing methods focus on implicitly differentiating Karush–Kuhn–Tucker (KKT) conditions in a way that requires expensive computations on the Jacobian matrix, which can be slow and memory-intensive. In this paper, we developed a new framework, named Alternating Differentiation (Alt-Diff), that differentiates optimization problems (here, specifically in the form of convex optimization problems with polyhedral constraints) in a fast and recursive way. Alt-Diff decouples the differentiation procedure into a primal update and a dual update in an alternating way. Accordingly, Alt-Diff substantially decreases the dimensions of the Jacobian matrix especially for optimization with large-scale constraints and thus increases the computational speed of implicit differentiation. We show that the gradients obtained by Alt-Diff are consistent with those obtained by differentiating KKT conditions. In addition, we propose to truncate Alt-Diff to further accelerate the computational speed. Under some standard assumptions, we show that the truncation error of gradients is upper bounded by the same order of variables' estimation error. Therefore, Alt-Diff can be truncated to further increase computational speed without sacrificing much accuracy. A series of comprehensive experiments validate the superiority of Alt-Diff. | https://openreview.net/pdf/8c99dd22208d44e3603aee506e0f2898500ec378.pdf |
Out-of-distribution Detection with Implicit Outlier Transformation | https://openreview.net/forum?id=hdghx6wbGuD | https://openreview.net/forum?id=hdghx6wbGuD | Qizhou Wang,Junjie Ye,Feng Liu,Quanyu Dai,Marcus Kalander,Tongliang Liu,Jianye HAO,Bo Han | ICLR 2023,Poster | Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection, enhancing detection capability via model fine-tuning with surrogate OOD data. However, surrogate data typically deviate from test OOD data. Thus, the performance of OE when facing unseen OOD data, can be weaken. To address this issue, we propose a novel OE-based approach that makes the model perform well for unseen OOD situations, even for unseen OOD cases. It leads to a min-max learning scheme---searching to synthesize OOD data that leads to worst judgments and learning from such OOD data for the uniform performance in OOD detection. In our realization, these worst OOD data are synthesized by transforming original surrogate ones, where the associated transform functions are learned implicitly based on our novel insight that model perturbation leads to data transformation. Our methodology offers an efficient way of synthesizing OOD data, which can further benefit the detection model, besides the surrogate OOD data. We conduct extensive experiments under various OOD detection setups, demonstrating the effectiveness of our method against its advanced counterparts. | https://openreview.net/pdf/01c1df8311eac17049936b939528937c192b0812.pdf |
Extracting Robust Models with Uncertain Examples | https://openreview.net/forum?id=cMAjKYftNwx | https://openreview.net/forum?id=cMAjKYftNwx | Guanlin Li,Guowen Xu,Shangwei Guo,Han Qiu,Jiwei Li,Tianwei Zhang | ICLR 2023,Poster | Model extraction attacks are proven to be a severe privacy threat to Machine Learning as a Service (MLaaS). A variety of techniques have been designed to steal a remote machine learning model with high accuracy and fidelity. However, how to extract a robust model with similar resilience against adversarial attacks is never investigated. This paper presents the first study toward this goal. We first analyze those existing extraction solutions either fail to maintain the model accuracy or model robustness or lead to the robust overfitting issue. Then we propose Boundary Entropy Searching Thief (BEST), a novel model extraction attack to achieve both accuracy and robustness extraction under restricted attack budgets. BEST generates a new kind of uncertain examples for querying and reconstructing the victim model. These samples have uniform confidence scores across different classes, which can perfectly balance the trade-off between model accuracy and robustness. Extensive experiments demonstrate that BEST outperforms existing attack methods over different datasets and model architectures under limited data. It can also effectively invalidate state-of-the-art extraction defenses. | https://openreview.net/pdf/722f2b14072c35db58ad81fd31b4692034751c95.pdf |
Neural Groundplans: Persistent Neural Scene Representations from a Single Image | https://openreview.net/forum?id=Pza24zf9FpS | https://openreview.net/forum?id=Pza24zf9FpS | Prafull Sharma,Ayush Tewari,Yilun Du,Sergey Zakharov,Rares Andrei Ambrus,Adrien Gaidon,William T. Freeman,Fredo Durand,Joshua B. Tenenbaum,Vincent Sitzmann | ICLR 2023,Poster | We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable and immovable components of the scene. Motivated by the bird’s-eye-view (BEV) representation commonly used in vision and robotics, we propose conditional neural groundplans, ground-aligned 2D feature grids, as persistent and memory-efficient scene representations. Our method is trained self-supervised from unlabeled multi-view observations using differentiable rendering, and learns to complete geometry and appearance of occluded regions. In addition, we show that we can leverage multi-view videos at training time to learn to separately reconstruct static and movable components of the scene from a single image at test time. The ability to separately reconstruct movable objects enables a variety of downstream tasks using simple heuristics, such as extraction of object-centric 3D representations, novel view synthesis, instance-level segmentation, 3D bounding box prediction, and scene editing. This highlights the value of neural groundplans as a backbone for efficient 3D scene understanding models. | https://openreview.net/pdf/2e877547b8a62b0459ece4c3c0c9fff256045e77.pdf |
E-CRF: Embedded Conditional Random Field for Boundary-caused Class Weights Confusion in Semantic Segmentation | https://openreview.net/forum?id=g1GnnCI1OrC | https://openreview.net/forum?id=g1GnnCI1OrC | Jie Zhu,Huabin Huang,Banghuai Li,Leye Wang | ICLR 2023,Poster | Modern semantic segmentation methods devote much effect to adjusting image feature representations to improve the segmentation performance in various ways, such as architecture design, attention mechnism, etc. However, almost all those methods neglect the particularity of class weights (in the classification layer) in segmentation models. In this paper, we notice that the class weights of categories that tend to share many adjacent boundary pixels lack discrimination, thereby limiting the performance. We call this issue Boundary-caused Class Weights Confusion (BCWC). We try to focus on this problem and propose a novel method named Embedded Conditional Random Field (E-CRF) to alleviate it. E-CRF innovatively fuses the CRF into the CNN network as an organic whole for more effective end-to-end optimization. The reasons are two folds. It utilizes CRF to guide the message passing between pixels in high-level features to purify the feature representation of boundary pixels, with the help of inner pixels belonging to the same object. More importantly, it enables optimizing class weights from both scale and direction during backpropagation. We make detailed theoretical analysis to prove it. Besides, superpixel is integrated into E-CRF and served as an auxiliary to exploit the local object prior for more reliable message passing. Finally, our proposed method yields impressive results on ADE20K, Cityscapes, and Pascal Context datasets. | https://openreview.net/pdf/b721483faa1628cd8fd142a2df262b396c292ccf.pdf |
Sample Complexity of Nonparametric Off-Policy Evaluation on Low-Dimensional Manifolds using Deep Networks | https://openreview.net/forum?id=9x3CO0ZU9LR | https://openreview.net/forum?id=9x3CO0ZU9LR | Xiang Ji,Minshuo Chen,Mengdi Wang,Tuo Zhao | ICLR 2023,Poster | We consider the off-policy evaluation problem of reinforcement learning using deep convolutional neural networks. We analyze the deep fitted Q-evaluation method for estimating the expected cumulative reward of a target policy, when the data are generated from an unknown behavior policy. We show that, by choosing network size appropriately, one can leverage any low-dimensional manifold structure in the Markov decision process and obtain a sample-efficient estimator without suffering from the curse of high data ambient dimensionality. Specifically, we establish a sharp error bound for fitted Q-evaluation, which depends on the intrinsic dimension of the state-action space, the smoothness of Bellman operator, and a function class-restricted $\chi^2$-divergence. It is noteworthy that the restricted $\chi^2$-divergence measures the behavior and target policies' {\it mismatch in the function space}, which can be small even if the two policies are not close to each other in their tabular forms. We also develop a novel approximation result for convolutional neural networks in Q-function estimation. Numerical experiments are provided to support our theoretical analysis. | https://openreview.net/pdf/6ce075be5b8a6bc4f5f89e4d67cb652b91081627.pdf |
Stochastic Differentially Private and Fair Learning | https://openreview.net/forum?id=3nM5uhPlfv6 | https://openreview.net/forum?id=3nM5uhPlfv6 | Andrew Lowy,Devansh Gupta,Meisam Razaviyayn | ICLR 2023,Poster | Machine learning models are increasingly used in high-stakes decision-making systems. In such applications, a major concern is that these models sometimes discriminate against certain demographic groups such as individuals with certain race, gender, or age. Another major concern in these applications is the violation of the privacy of users. While fair learning algorithms have been developed to mitigate discrimination issues, these algorithms can still leak sensitive information, such as individuals’ health or financial records. Utilizing the notion of differential privacy (DP), prior works aimed at developing learning algorithms that are both private and fair. However, existing algorithms for DP fair learning are either not guaranteed to converge or require full batch of data in each iteration of the algorithm to converge. In this paper, we provide the first stochastic differentially private algorithm for fair learning that is guaranteed to converge. Here, the term “stochastic" refers to the fact that our proposed algorithm converges even when minibatches of data are used at each iteration (i.e. stochastic optimization). Our framework is flexible enough to permit different fairness notions, including demographic parity and equalized odds. In addition, our algorithm can be applied to non-binary classification tasks with multiple (non-binary) sensitive attributes. As a byproduct of our convergence analysis, we provide the first utility guarantee for a DP algorithm for solving nonconvex-strongly concave min-max problems. Our numerical experiments show that the proposed algorithm consistently offers significant performance gains over the state-of-the-art baselines, and can be applied to larger scale problems with non-binary target/sensitive attributes. | https://openreview.net/pdf/fc65ef7914e82ee94fba6f4e3211b110fd97f24b.pdf |
On The Inadequacy of Optimizing Alignment and Uniformity in Contrastive Learning of Sentence Representations | https://openreview.net/forum?id=MxvHVNukama | https://openreview.net/forum?id=MxvHVNukama | Zhijie Nie,Richong Zhang,Yongyi Mao | ICLR 2023,Poster | Contrastive learning is widely used in areas such as visual representation learning (VRL) and sentence representation learning (SRL). Considering the differences between VRL and SRL in terms of negative sample size and evaluation focus, we believe that the solid findings obtained in VRL may not be entirely carried over to SRL. In this work, we consider the suitability of the decoupled form of contrastive loss, i.e., alignment and uniformity, in SRL. We find a performance gap between sentence representations obtained by jointly optimizing alignment and uniformity on the STS task and those obtained using contrastive loss. Further, we find that the joint optimization of alignment and uniformity during training is prone to overfitting, which does not occur on the contrastive loss. Analyzing them based on the variation of the gradient norms, we find that there is a property of ``gradient dissipation'' in contrastive loss and believe that it is the key to preventing overfitting. We simulate similar "gradient dissipation" of contrastive loss on four optimization objectives of two forms, and achieve the same or even better performance than contrastive loss on the STS tasks, confirming our hypothesis. | https://openreview.net/pdf/a53c92c02df6e929732ff69807a56941d547b1e9.pdf |
Volumetric Optimal Transportation by Fast Fourier Transform | https://openreview.net/forum?id=EVrz7UM-ZDm | https://openreview.net/forum?id=EVrz7UM-ZDm | Na Lei,DONGSHENG An,Min Zhang,Xiaoyin Xu,David Gu | ICLR 2023,Poster | The optimal transportation map finds the most economical way to transport one probability measure to another, and it has been applied in a broad range of applications in machine learning and computer vision. By the Brenier theory, computing the optimal transport map is equivalent to solving a Monge-Amp\`ere equation, which is highly non-linear. Therefore, the computation of optimal transportation maps is intrinsically challenging.
In this work, we propose a novel and powerful method, the FFT-OT (fast Fourier transform-optimal transport), to compute the 3-dimensional OT problems. The method is based on several key ideas: first, the Monge-Amp\`ere equation is linearized to a sequence of linear elliptic PDEs with spacial and temporal variant coefficients; second, the obliqueness property of optimal transportation maps is reformulated as a Neumann boundary condition; and third, the variant coefficient elliptic PDEs are approximated by constant coefficient elliptic PDEs and solved by FFT on GPUs. We also prove that the algorithm converges linearly, namely the approximation error decreases exponentially fast. Experimental results show that the FFT-OT algorithm is more than a hundred times faster than the conventional methods based on the convex geometry. Furthermore, the method can be directly applied for sampling from complex 3D density functions in machine learning and magnifying the volumetric data in medical imaging. | https://openreview.net/pdf/286c7fad917439bf5f5b68ba9b27b2a1dbe555a3.pdf |
GFlowNets and variational inference | https://openreview.net/forum?id=uKiE0VIluA- | https://openreview.net/forum?id=uKiE0VIluA- | Nikolay Malkin,Salem Lahlou,Tristan Deleu,Xu Ji,Edward J Hu,Katie E Everett,Dinghuai Zhang,Yoshua Bengio | ICLR 2023,Poster | This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs. We demonstrate that, in certain cases, VI algorithms are equivalent to special cases of GFlowNets in the sense of equality of expected gradients of their learning objectives. We then point out the differences between the two families and show how these differences emerge experimentally. Notably, GFlowNets, which borrow ideas from reinforcement learning, are more amenable than VI to off-policy training without the cost of high gradient variance induced by importance sampling. We argue that this property of GFlowNets can provide advantages for capturing diversity in multimodal target distributions. Code: https://github.com/GFNOrg/GFN_vs_HVI. | https://openreview.net/pdf/7fcf83ff3797f23ba25909993d4d7281d23ca332.pdf |
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion | https://openreview.net/forum?id=zlwBI2gQL3K | https://openreview.net/forum?id=zlwBI2gQL3K | Han Wu,Jie Yin,Bala Rajaratnam,Jianyuan Guo | ICLR 2023,Poster | Knowledge graphs (KGs) are powerful in terms of their inference abilities, but are also notorious for their incompleteness and long-tail distribution of relations. To address these challenges and expand the coverage of KGs, few-shot KG completion aims to make predictions for triplets involving novel relations when only a few training triplets are provided as reference. Previous methods have focused on designing local neighbor aggregators to learn entity-level information and/or imposing sequential dependency assumption at the triplet level to learn meta relation information. However, pairwise triplet-level interactions and context-level relational information have been largely overlooked for learning meta representations of few-shot relations. In this paper, we propose a hierarchical relational learning method (HiRe) for few-shot KG completion. By jointly capturing three levels of relational information (entity-level, triplet-level and context-level), HiRe can effectively learn and refine the meta representation of few-shot relations, and consequently generalize well to new unseen relations. Extensive experiments on two benchmark datasets validate the superiority of HiRe over state-of-the-art methods. The code of HiRe can be found in supplementary material and will be released after acceptance. | https://openreview.net/pdf/7fb3c4eed318cb32b972d300fb5fe64530416a25.pdf |
Function-Consistent Feature Distillation | https://openreview.net/forum?id=pgHNOcxEdRI | https://openreview.net/forum?id=pgHNOcxEdRI | Dongyang Liu,Meina Kan,Shiguang Shan,Xilin CHEN | ICLR 2023,Poster | Feature distillation makes the student mimic the intermediate features of the teacher. Nearly all existing feature-distillation methods use L2 distance or its slight variants as the distance metric between teacher and student features. However, while L2 distance is isotropic w.r.t. all dimensions, the neural network’s operation on different dimensions is usually anisotropic, i.e., perturbations with the same 2-norm but in different dimensions of intermediate features lead to changes in the final output with largely different magnitude. Considering this, we argue that the similarity between teacher and student features should \textit{not} be measured merely based on their appearance (i.e., L2 distance), but should, more importantly, be measured by their difference in function, namely how later layers of the network will read, decode, and process them. Therefore, we propose Function-Consistent Feature Distillation (FCFD), which explicitly optimizes the functional similarity between teacher and student features. The core idea of FCFD is to make teacher and student features not only numerically similar, but more importantly produce similar outputs when fed to the later part of the same network. With FCFD, the student mimics the teacher more faithfully and learns more from the teacher. Extensive experiments on image classification and object detection demonstrate the superiority of FCFD to existing methods. Furthermore, we can combine FCFD with many existing methods to obtain even higher accuracy. Our codes are available at https://github.com/LiuDongyang6/FCFD. | https://openreview.net/pdf/49cf06e39cb1b464f0f63c3e11a2a9c7e6614a2c.pdf |
The Devil is in the Wrongly-classified Samples: Towards Unified Open-set Recognition | https://openreview.net/forum?id=xLr0I_xYGAs | https://openreview.net/forum?id=xLr0I_xYGAs | Jun CEN,Di Luan,Shiwei Zhang,Yixuan Pei,Yingya Zhang,Deli Zhao,Shaojie Shen,Qifeng Chen | ICLR 2023,Poster | Open-set Recognition (OSR) aims to identify test samples whose classes are not seen during the training process. Recently, Unified Open-set Recognition (UOSR) has been proposed to reject not only unknown samples but also known but wrongly classified samples, which tends to be more practical in real-world applications. In this paper, we deeply analyze the UOSR task under different training and evaluation settings to shed light on this promising research direction. For this purpose, we first evaluate the UOSR performance of several OSR methods and show a significant finding that the uncertainty distribution of almost all these methods is actually closer to the expectation of UOSR than OSR. We show that the reason lies in the known but wrongly classified samples, as their uncertainty distribution is extremely close to unknown samples rather than known and correctly classified samples. Second, we analyze how the two training settings of OSR (i.e., pre-training and outlier exposure) influence the UOSR. We find although they are both beneficial for distinguishing known and correctly classified samples from unknown samples, pre-training is also helpful for identifying known but wrongly classified samples while outlier exposure is not. In addition to different training settings, we also formulate a new evaluation setting for UOSR which is called few-shot UOSR, where only one or five samples per unknown class are available during evaluation to help identify unknown samples. We propose FS-KNNS for the few-shot UOSR to achieve state-of-the-art performance under all settings. | https://openreview.net/pdf/b7fff2c7db3869908cb083516d6b3d4f29eeabbc.pdf |
MCAL: Minimum Cost Human-Machine Active Labeling | https://openreview.net/forum?id=1FxRPKrH8bw | https://openreview.net/forum?id=1FxRPKrH8bw | Hang Qiu,Krishna Chintalapudi,Ramesh Govindan | ICLR 2023,Poster | Today, ground-truth generation uses data sets annotated by cloud-based annotation services. These services rely on human annotation, which can be prohibitively expensive. In this paper, we consider the problem of hybrid human-machine labeling, which trains a classifier to accurately auto-label part of the data set. However, training the classifier can be expensive too. We propose an iterative approach that minimizes total overall cost by, at each step, jointly determining which samples to label using humans and which to label using the trained classifier. We validate our approach on well known public data sets such as Fashion-MNIST, CIFAR-10, CIFAR-100, and ImageNet. In some cases, our approach has 6× lower overall cost relative to human labeling the entire data set, and is always cheaper than the cheapest competing strategy. | https://openreview.net/pdf/6e91bc4e335c6dc641df9d7334a0c3ba83c63007.pdf |
Learnable Topological Features For Phylogenetic Inference via Graph Neural Networks | https://openreview.net/forum?id=hVVUY7p64WL | https://openreview.net/forum?id=hVVUY7p64WL | Cheng Zhang | ICLR 2023,Poster | Structural information of phylogenetic tree topologies plays an important role in phylogenetic inference. However, finding appropriate topological structures for specific phylogenetic inference tasks often requires significant design effort and domain expertise. In this paper, we propose a novel structural representation method for phylogenetic inference based on learnable topological features. By combining the raw node features that minimize the Dirichlet energy with modern graph representation learning techniques, our learnable topological features can provide efficient structural information of phylogenetic trees that automatically adapts to different downstream tasks without requiring domain expertise. We demonstrate the effectiveness and efficiency of our method on a simulated data tree probability estimation task and a benchmark of challenging real data variational Bayesian phylogenetic inference problems. | https://openreview.net/pdf/94ddddee68e974505f45bc9249fcd649c1167f2c.pdf |
Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes | https://openreview.net/forum?id=woa783QMul | https://openreview.net/forum?id=woa783QMul | Fengda Zhang,Kun Kuang,Long Chen,Yuxuan Liu,Chao Wu,Jun Xiao | ICLR 2023,Poster | Learning high-quality representation is important and essential for visual recognition. Unfortunately, traditional representation learning suffers from fairness issues since the model may learn information of sensitive attributes. Recently, a series of studies have been proposed to improve fairness by explicitly decorrelating target labels and sensitive attributes. Most of these methods, however, rely on the assumption that fully annotated labels on target variable and sensitive attributes are available, which is unrealistic due to the expensive annotation cost. In this paper, we investigate a novel and practical problem of Fair Unsupervised Representation Learning with Partially annotated Sensitive labels (FURL-PS). FURL-PS has two key challenges: 1) how to make full use of the samples that are not annotated with sensitive attributes; 2) how to eliminate bias in the dataset without target labels. To address these challenges, we propose a general Fairness-aware Contrastive Learning (FairCL) framework consisting of two stages. Firstly, we generate contrastive sample pairs, which share the same visual information apart from sensitive attributes, for each instance in the original dataset. In this way, we construct a balanced and unbiased dataset. Then, we execute fair contrastive learning by closing the distance between representations of contrastive sample pairs. Besides, we also propose an unsupervised way to balance the utility and fairness of learned representations by feature reweighting. Extensive experimental results illustrate the effectiveness of our method in terms of fairness and utility, even with very limited sensitive attributes and serious data bias. | https://openreview.net/pdf/31952982a3b3cde1de1721a4dacb49a391a21368.pdf |
Rotamer Density Estimator is an Unsupervised Learner of the Effect of Mutations on Protein-Protein Interaction | https://openreview.net/forum?id=_X9Yl1K2mD | https://openreview.net/forum?id=_X9Yl1K2mD | Shitong Luo,Yufeng Su,Zuofan Wu,Chenpeng Su,Jian Peng,Jianzhu Ma | ICLR 2023,Poster | Protein-protein interactions are crucial to many biological processes, and predicting the effect of amino acid mutations on binding is important for protein engineering. While data-driven approaches using deep learning have shown promise, the scarcity of annotated experimental data remains a major challenge. In this work, we propose a new approach that predicts mutational effects on binding using the change in conformational flexibility of the protein-protein interface. Our approach, named Rotamer Density Estimator (RDE), employs a flow-based generative model to estimate the probability distribution of protein side-chain conformations and uses entropy to measure flexibility. RDE is trained solely on protein structures and does not require the supervision of experimental values of changes in binding affinities. Furthermore, the unsupervised representations extracted by RDE can be used for downstream neural network predictions with even greater accuracy. Our method outperforms empirical energy functions and other machine learning-based approaches. | https://openreview.net/pdf/b9cb2b34bed45fb8e25ce997c680d28d8e7e2e1c.pdf |
Dilated convolution with learnable spacings | https://openreview.net/forum?id=Q3-1vRh3HOA | https://openreview.net/forum?id=Q3-1vRh3HOA | Ismail Khalfaoui Hassani,Thomas Pellegrini,Timothée Masquelier | ICLR 2023,Poster | Recent works indicate that convolutional neural networks (CNN) need large receptive fields (RF) to compete with visual transformers and their attention mechanism. In CNNs, RFs can simply be enlarged by increasing the convolution kernel sizes. Yet the number of trainable parameters, which scales quadratically with the kernel's size in the 2D case, rapidly becomes prohibitive, and the training is notoriously difficult. This paper presents a new method to increase the RF size without increasing the number of parameters. The dilated convolution (DC) has already been proposed for the same purpose. DC can be seen as a convolution with a kernel that contains only a few non-zero elements placed on a regular grid. Here we present a new version of the DC in which the spacings between the non-zero elements, or equivalently their positions, are no longer fixed but learnable via backpropagation thanks to an interpolation technique. We call this method “Dilated Convolution with Learnable Spacings” (DCLS) and generalize it to the n-dimensional convolution case. However, our main focus here will be on the 2D case. We first tried our approach on ResNet50: we drop-in replaced the standard convolutions with DCLS ones, which increased the accuracy of ImageNet1k classification at iso-parameters, but at the expense of the throughput. Next, we used the recent ConvNeXt state-of-the-art convolutional architecture and drop-in replaced the depthwise convolutions with DCLS ones. This not only increased the accuracy of ImageNet1k classification but also of typical downstream and robustness tasks, again at iso-parameters but this time with negligible cost on throughput, as ConvNeXt uses separable convolutions. Conversely, classic DC led to poor performance with both ResNet50 and ConvNeXt. The code of the method is based on PyTorch and available at: https://github.com/K-H-Ismail/Dilated-Convolution-with-Learnable-Spacings-PyTorch. | https://openreview.net/pdf/5174fc665cbd0aaf3c59251a8dfb5592a767a798.pdf |
PatchDCT: Patch Refinement for High Quality Instance Segmentation | https://openreview.net/forum?id=t9Zd7Oi5JPl | https://openreview.net/forum?id=t9Zd7Oi5JPl | Qinrou Wen,Jirui Yang,Xue Yang,Kewei Liang | ICLR 2023,Poster | High-quality instance segmentation has shown emerging importance in computer vision. Without any refinement, DCT-Mask directly generates high-resolution masks by compressed vectors. To further refine masks obtained by compressed vectors, we propose for the first time a compressed vector based multi-stage refinement framework. However, the vanilla combination does not bring significant gains, because changes in some elements of the DCT vector will affect the prediction of the entire mask. Thus, we propose a simple and novel method named PatchDCT, which separates the mask decoded from a DCT vector into several patches and refines each patch by the designed classifier and regressor. Specifically, the classifier is used to distinguish mixed patches from all patches, and to correct previously mispredicted foreground and background patches. In contrast, the regressor is used for DCT vector prediction of mixed patches, further refining the segmentation quality at boundary locations. Experiments on COCO show that our method achieves 2.0\%, 3.2\%, 4.5\% AP and 3.4\%, 5.3\%, 7.0\% Boundary AP improvements over Mask-RCNN on COCO, LVIS, and Cityscapes, respectively. It also surpasses DCT-Mask by 0.7\%, 1.1\%, 1.3\% AP and 0.9\%, 1.7\%, 4.2\% Boundary AP on COCO, LVIS and Cityscapes. Besides, the performance of PatchDCT is also competitive with other state-of-the-art methods. | https://openreview.net/pdf/2f7aeac3d7d77bb1af67bdb17bee0bf2b83cfacb.pdf |
ChiroDiff: Modelling chirographic data with Diffusion Models | https://openreview.net/forum?id=1ROAstc9jv | https://openreview.net/forum?id=1ROAstc9jv | Ayan Das,Yongxin Yang,Timothy Hospedales,Tao Xiang,Yi-Zhe Song | ICLR 2023,Poster | Generative modelling over continuous-time geometric constructs, a.k.a $chirographic\ data$ such as handwriting, sketches, drawings etc., have been accomplished through autoregressive distributions. Such strictly-ordered discrete factorization however falls short of capturing key properties of chirographic data -- it fails to build holistic understanding of the temporal concept due to one-way visibility (causality). Consequently, temporal data has been modelled as discrete token sequences of fixed sampling rate instead of capturing the true underlying concept. In this paper, we introduce a powerful model-class namely Denoising\ Diffusion\ Probabilistic\ Models or DDPMs for chirographic data that specifically addresses these flaws. Our model named "ChiroDiff", being non-autoregressive, learns to capture holistic concepts and therefore remains resilient to higher temporal sampling rate up to a good extent. Moreover, we show that many important downstream utilities (e.g. conditional sampling, creative mixing) can be flexibly implemented using ChiroDiff. We further show some unique use-cases like stochastic vectorization, de-noising/healing, abstraction are also possible with this model-class. We perform quantitative and qualitative evaluation of our framework on relevant datasets and found it to be better or on par with competing approaches. | https://openreview.net/pdf/2df012fbedfa6bf4cafce1218732561500298315.pdf |
Real-Time Image Demoir$\acute{e}$ing on Mobile Devices | https://openreview.net/forum?id=PmP_sf3JkrH | https://openreview.net/forum?id=PmP_sf3JkrH | Yuxin Zhang,Mingbao Lin,Xunchao Li,Han Liu,Guozhi Wang,Fei Chao,Ren Shuai,Yafei Wen,Xiaoxin Chen,Rongrong Ji | ICLR 2023,Poster | Moir$\acute{e}$ patterns appear frequently when taking photos of digital screens, drastically degrading the image quality. Despite the advance of CNNs in image demoir$\acute{e}$ing, existing networks are with heavy design, causing massive computation burden for mobile devices. In this paper, we launch the first study on accelerating demoir$\acute{e}$ing networks and propose a dynamic demoir$\acute{e}$ing acceleration method (DDA) towards a real-time deployment on mobile devices. Our stimulus stems from a simple-yet-universal fact that moir${\'e}$ patterns often unbalancedly distribute across an image. Consequently, excessive computation is wasted upon non-moir$\acute{e}$ areas. Therefore, we reallocate computation costs in proportion to the complexity of image patches. In order to achieve this aim, we measure the complexity of an image patch by a novel moir$\acute{e}$ prior that considers both colorfulness and frequency information of moir$\acute{e}$ patterns. Then, we restore higher-complex image patches using larger networks and the lower-complex ones are assigned with smaller networks to relieve the computation burden. At last, we train all networks in a parameter-shared supernet paradigm to avoid additional parameter burden. Extensive experiments on several benchmarks demonstrate the efficacy of our DDA. In addition, the acceleration evaluated on the VIVO X80 Pro smartphone equipped with the chip of Snapdragon 8 Gen 1 also shows that our method can drastically reduce the inference time, leading to a real-time image demoir$\acute{e}$ing on mobile devices. Source codes and models are released at https://github.com/zyxxmu/DDA.
| https://openreview.net/pdf/57318515af071cd3d12f09ea76595aaa2050c078.pdf |
Cross-Level Distillation and Feature Denoising for Cross-Domain Few-Shot Classification | https://openreview.net/forum?id=Kn-HA8DFik | https://openreview.net/forum?id=Kn-HA8DFik | Hao ZHENG,Runqi Wang,Jianzhuang Liu,Asako Kanezaki | ICLR 2023,Poster | The conventional few-shot classification aims at learning a model on a large labeled base dataset and rapidly adapting to a target dataset that is from the same distribution as the base dataset. However, in practice, the base and the target datasets of few-shot classification are usually from different domains, which is the problem of cross-domain few-shot classification. We tackle this problem by making a small proportion of unlabeled images in the target domain accessible in the training stage. In this setup, even though the base data are sufficient and labeled, the large domain shift still makes transferring the knowledge from the base dataset difficult. We meticulously design a cross-level knowledge distillation method, which can strengthen the ability of the model to extract more discriminative features in the target dataset by guiding the network's shallow layers to learn higher-level information. Furthermore, in order to alleviate the overfitting in the evaluation stage, we propose a feature denoising operation which can reduce the feature redundancy and mitigate overfitting. Our approach can surpass the previous state-of-the-art method, Dynamic-Distillation, by 5.44% on 1-shot and 1.37% on 5-shot classification tasks on average in the BSCD-FSL benchmark. The implementation code will be available at https://gitee.com/mindspore/models/tree/master/research/cv/CLDFD. | https://openreview.net/pdf/cc48f6aebf1eb9429f8570a25b548a0e9c3e85a8.pdf |
DELTA: DEGRADATION-FREE FULLY TEST-TIME ADAPTATION | https://openreview.net/forum?id=eGm22rqG93 | https://openreview.net/forum?id=eGm22rqG93 | Bowen Zhao,Chen Chen,Shu-Tao Xia | ICLR 2023,Poster | Fully test-time adaptation aims at adapting a pre-trained model to the test stream during real-time inference, which is urgently required when the test distribution differs from the training distribution. Several efforts have been devoted to improving adaptation performance. However, we find that two unfavorable defects are concealed in the prevalent adaptation methodologies like test-time batch normalization (BN) and self-learning. First, we reveal that the normalization statistics in test-time BN are completely affected by the currently received test samples, resulting in inaccurate estimates. Second, we show that during test-time adaptation, the parameter update is biased towards some dominant classes. In addition to the extensively studied test stream with independent and class-balanced samples, we further observe that the defects can be exacerbated in more complicated test environments, such as (time) dependent or class-imbalanced data. We observe that previous approaches work well in certain scenarios while show performance degradation in others due to their faults. In this paper, we provide a plug-in solution called DELTA for Degradation-freE fuLly Test-time Adaptation, which consists of two components: (i) Test-time Batch Renormalization (TBR), introduced to improve the estimated normalization statistics. (ii) Dynamic Online re-weighTing (DOT), designed to address the class bias within optimization. We investigate various test-time adaptation methods on three commonly used datasets with four scenarios, and a newly introduced real-world dataset. DELTA can help them deal with all scenarios simultaneously, leading to SOTA performance. | https://openreview.net/pdf/aecdf7f8281e50fbbedf1b0bdb5bc5401421aa7b.pdf |
Bit-Pruning: A Sparse Multiplication-Less Dot-Product | https://openreview.net/forum?id=YUDiZcZTI8 | https://openreview.net/forum?id=YUDiZcZTI8 | Yusuke Sekikawa,Shingo Yashima | ICLR 2023,Poster | Dot-product is a central building block in neural networks.
However, multiplication ($\texttt{mult}$) in dot-product consumes intensive energy and space costs that challenge deployment on resource-constrained edge devices.
In this study, we realize energy-efficient neural networks by exploiting a $\texttt{mult}$-less, sparse dot-product. We first reformulate a dot-product between an integer weight and activation into an equivalent operation comprised of additions followed by bit-shifts ($\texttt{add-shift-add}$).
In this formulation, the number of $\texttt{add}$ operations equals the number of bits of the integer weight in binary format.
Leveraging this observation, we propose Bit-Pruning, which removes unnecessary bits in each weight value during training to reduce the energy consumption of $\texttt{add-shift-add}$. Bit-Pruning can be seen as soft Weight-Pruning as it prunes bits, not the whole weight element.
In extensive experiments, we demonstrate that sparse $\texttt{mult}$-less networks trained with Bit-Pruning show a better accuracy-energy trade-off than sparse $\texttt{mult}$ networks trained with Weight-Pruning. | https://openreview.net/pdf/7d8fb966d54a91c95760a29ae834a67169e93baa.pdf |
kNN-Diffusion: Image Generation via Large-Scale Retrieval | https://openreview.net/forum?id=x5mtJD2ovc | https://openreview.net/forum?id=x5mtJD2ovc | Shelly Sheynin,Oron Ashual,Adam Polyak,Uriel Singer,Oran Gafni,Eliya Nachmani,Yaniv Taigman | ICLR 2023,Poster | Recent text-to-image models have achieved impressive results. However, since they require large-scale datasets of text-image pairs, it is impractical to train them on new domains where data is scarce or not labeled.
In this work, we propose using large-scale retrieval methods, in particular, efficient k-Nearest-Neighbors (kNN), which offers novel capabilities: (1) training a substantially small and efficient text-to-image diffusion model using only pre-trained multi-modal embeddings, but without an explicit text-image dataset, (2) generating out-of-distribution images by simply swapping the retrieval database at inference time, and (3) performing text-driven local semantic manipulations while preserving object identity. To demonstrate the robustness of our method, we apply our kNN approach on two state-of-the-art diffusion backbones, and show results on several different datasets. As evaluated by human studies and automatic metrics, our method achieves state-of-the-art results compared to existing approaches that train text-to-image generation models using images-only dataset. | https://openreview.net/pdf/b8975b72f550b7061f9e42248a7f29257a2c71fc.pdf |
Decompose to Generalize: Species-Generalized Animal Pose Estimation | https://openreview.net/forum?id=nQai_B1Zrt | https://openreview.net/forum?id=nQai_B1Zrt | Guangrui Li,Yifan Sun,Zongxin Yang,Yi Yang | ICLR 2023,Poster | This paper challenges the cross-species generalization problem for animal pose estimation, aiming to learn a pose estimator that can be well generalized to novel species. We find the relation between different joints is important with two-fold impact: 1) on the one hand, some relation is consistent across all the species and may help two joints mutually confirm each other, e.g., the eyes help confirm the nose and vice versa because they are close in all species. 2) on the other hand, some relation is inconsistent for different species due to the species variation and may bring severe distraction rather than benefit. With these two insights, we propose a Decompose-to-Generalize (D-Gen) pose estimation method to break the inconsistent relations while preserving the consistent ones. Specifically, D-Gen first decomposes the body joints into several joint concepts so that each concept contains multiple closely-related joints. Given these joint concepts, D-Gen 1) promotes the interaction between intra-concept joints to enhance their reliable mutual confirmation, and 2) suppresses the interaction between inter-concept joints to prohibit their mutual distraction. Importantly, we explore various decomposition approaches, i.e., heuristic, geometric and attention-based approaches. Experimental results show that all these decomposition manners yield reasonable joint concepts and substantially improve cross-species generalization (and the attention-based approach is the best). | https://openreview.net/pdf/a3b3f825344de0536ec200e556e03d3bbffece49.pdf |
IDEAL: Query-Efficient Data-Free Learning from Black-Box Models | https://openreview.net/forum?id=ConT6H7MWL | https://openreview.net/forum?id=ConT6H7MWL | Jie Zhang,Chen Chen,Lingjuan Lyu | ICLR 2023,Poster | Knowledge Distillation (KD) is a typical method for training a lightweight student model with the help of a well-trained teacher model.
However, most KD methods require access to either the teacher's training data or model parameter, which is unrealistic. To tackle this problem, recent works study KD under data-free and black-box settings. Nevertheless, these works require a large number of queries to the teacher model, which incurs significant monetary and computational costs. To address these problems, we propose a novel method called \emph{query-effIcient Data-free lEarning from blAck-box modeLs} (IDEAL), which aims to query-efficiently learn from black-box model APIs to train a good student without any real data. In detail, IDEAL trains the student model in two stages: data generation and model distillation. Note that IDEAL does not require any query in the data generation stage and queries the teacher only once for each sample in the distillation stage. Extensive experiments on various real-world datasets show the effectiveness of the proposed IDEAL. For instance, IDEAL can improve the performance of the best baseline method DFME by 5.83\% on CIFAR10 dataset with only $0.02\times$ the query budget of DFME. | https://openreview.net/pdf/35d098045a1896058e0813e94ff5730165c03247.pdf |
Trainability Preserving Neural Pruning | https://openreview.net/forum?id=AZFvpnnewr | https://openreview.net/forum?id=AZFvpnnewr | Huan Wang,Yun Fu | ICLR 2023,Poster | Many recent works have shown trainability plays a central role in neural network pruning -- unattended broken trainability can lead to severe under-performance and unintentionally amplify the effect of retraining learning rate, resulting in biased (or even misinterpreted) benchmark results. This paper introduces trainability preserving pruning (TPP), a scalable method to preserve network trainability against pruning, aiming for improved pruning performance and being more robust to retraining hyper-parameters (e.g., learning rate). Specifically, we propose to penalize the gram matrix of convolutional filters to decorrelate the pruned filters from the retained filters. In addition to the convolutional layers, per the spirit of preserving the trainability of the whole network, we also propose to regularize the batch normalization parameters (scale and bias). Empirical studies on linear MLP networks show that TPP can perform on par with the oracle trainability recovery scheme. On nonlinear ConvNets (ResNet56/VGG19) on CIFAR10/100, TPP outperforms the other counterpart approaches by an obvious margin. Moreover, results on ImageNet-1K with ResNets suggest that TPP consistently performs more favorably against other top-performing structured pruning approaches. Code: https://github.com/MingSun-Tse/TPP. | https://openreview.net/pdf/1297e3fec8dd4a980666cce03de07123009695b3.pdf |
Diagnosing and Rectifying Vision Models using Language | https://openreview.net/forum?id=D-zfUK7BR6c | https://openreview.net/forum?id=D-zfUK7BR6c | Yuhui Zhang,Jeff Z. HaoChen,Shih-Cheng Huang,Kuan-Chieh Wang,James Zou,Serena Yeung | ICLR 2023,Poster | Recent multi-modal contrastive learning models have demonstrated the ability to learn an embedding space suitable for building strong vision classifiers, by leveraging the rich information in large-scale image-caption datasets. Our work highlights a distinct advantage of this multi-modal embedding space: the ability to diagnose vision classifiers through natural language. The traditional process of diagnosing model behaviors in deployment settings involves labor-intensive data acquisition and annotation. Our proposed method can discover high-error data slices, identify influential attributes and further rectify undesirable model behaviors, without requiring any visual data. Through a combination of theoretical explanation and empirical verification, we present conditions under which classifiers trained on embeddings from one modality can be equivalently applied to embeddings from another modality. On a range of image datasets with known error slices, we demonstrate that our method can effectively identify the error slices and influential attributes, and can further use language to rectify failure modes of the classifier. | https://openreview.net/pdf/abc659a15fb9f9adb64485d7e5837a76f8f5f216.pdf |
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style | https://openreview.net/forum?id=boNyg20-JDm | https://openreview.net/forum?id=boNyg20-JDm | Zhuo Huang,Xiaobo Xia,Li Shen,Bo Han,Mingming Gong,Chen Gong,Tongliang Liu | ICLR 2023,Poster | Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, such a problem has drawn much attention. However, current methods lack a full understanding of different types of OOD data: there are benign OOD data that can be properly adapted to enhance the learning performance, while other malign OOD data would severely degenerate the classification result. To Harness OOD data, this paper proposes HOOD method that can leverage the content and style from each image instance to identify benign and malign OOD data. Particularly, we design a variational inference framework to causally disentangle content and style features by constructing a structural causal model. Subsequently, we augment the content and style through an intervention process to produce malign and benign OOD data, respectively. The benign OOD data contain novel styles but hold our interested contents, and they can be leveraged to help train a style-invariant model. In contrast, the malign OOD data inherit unknown contents but carry familiar styles, by detecting them can improve model robustness against deceiving anomalies. Thanks to the proposed novel disentanglement and data augmentation techniques, HOOD can effectively deal with OOD examples in unknown and open environments, whose effectiveness is empirically validated in three typical OOD applications including OOD detection, open-set semi-supervised learning, and open-set domain adaptation. | https://openreview.net/pdf/ead13866b1d8951f087779370b079c54800f219e.pdf |
DropIT: Dropping Intermediate Tensors for Memory-Efficient DNN Training | https://openreview.net/forum?id=Kn6i2BZW69w | https://openreview.net/forum?id=Kn6i2BZW69w | Joya Chen,Kai Xu,Yuhui Wang,Yifei Cheng,Angela Yao | ICLR 2023,Poster | A standard hardware bottleneck when training deep neural networks is GPU memory. The bulk of memory is occupied by caching intermediate tensors for gradient computation in the backward pass. We propose a novel method to reduce this footprint - Dropping Intermediate Tensors (DropIT). DropIT drops min-k elements of the intermediate tensors and approximates gradients from the sparsified tensors in the backward pass. Theoretically, DropIT reduces noise on estimated gradients and therefore has a higher rate of convergence than vanilla-SGD. Experiments show that we can drop up to 90\% of the intermediate tensor elements in fully-connected and convolutional layers while achieving higher testing accuracy for Visual Transformers and Convolutional Neural Networks on various tasks (e.g., classification, object detection, instance segmentation). Our code and models are available at https://github.com/chenjoya/dropit. | https://openreview.net/pdf/6d97d95f31445e87005bb02cb950856f8164641f.pdf |
A Unified Framework for Soft Threshold Pruning | https://openreview.net/forum?id=cCFqcrq0d8 | https://openreview.net/forum?id=cCFqcrq0d8 | Yanqi Chen,Zhengyu Ma,Wei Fang,Xiawu Zheng,Zhaofei Yu,Yonghong Tian | ICLR 2023,Poster | Soft threshold pruning is among the cutting-edge pruning methods with state-of-the-art performance. However, previous methods either perform aimless searching on the threshold scheduler or simply set the threshold trainable, lacking theoretical explanation from a unified perspective. In this work, we reformulate soft threshold pruning as an implicit optimization problem solved using the Iterative Shrinkage-Thresholding Algorithm (ISTA), a classic method from the fields of sparse recovery and compressed sensing. Under this theoretical framework, all threshold tuning strategies proposed in previous studies of soft threshold pruning are concluded as different styles of tuning $L_1$-regularization term. We further derive an optimal threshold scheduler through an in-depth study of threshold scheduling based on our framework. This scheduler keeps $L_1$-regularization coefficient stable, implying a time-invariant objective function from the perspective of optimization. In principle, the derived pruning algorithm could sparsify any mathematical model trained via SGD. We conduct extensive experiments and verify its state-of-the-art performance on both Artificial Neural Networks (ResNet-50 and MobileNet-V1) and Spiking Neural Networks (SEW ResNet-18) on ImageNet datasets. On the basis of this framework, we derive a family of pruning methods, including sparsify-during-training, early pruning, and pruning at initialization. The code is available at https://github.com/Yanqi-Chen/LATS. | https://openreview.net/pdf/41e585b2744d3953cd611130faae8255fe601915.pdf |
TaskPrompter: Spatial-Channel Multi-Task Prompting for Dense Scene Understanding | https://openreview.net/forum?id=-CwPopPJda | https://openreview.net/forum?id=-CwPopPJda | Hanrong Ye,Dan Xu | ICLR 2023,Poster | Learning effective representations simultaneously from multiple tasks in a unified network framework is a fundamental paradigm for multi-task dense visual scene understanding. This requires joint modeling (i) task-generic and (ii) task-specific representations, and (iii) cross-task representation interactions. Existing works typically model these three perspectives with separately designed structures, using shared network modules for task-generic learning, different modules for task-specific learning, and establishing connections among these components for cross-task interactions. It is barely explored in the literature to model these three perspectives in each network layer in an end-to-end manner, which can not only minimize the effort of carefully designing empirical structures for the three multi-task representation learning objectives, but also greatly improve the representation learning capability of the multi-task network since all the model capacity will be used to optimize the three objectives together. In this paper, we propose TaskPrompter, a novel spatial-channel multi-task prompting transformer framework to achieve this target. Specifically, we design a set of spatial-channel task prompts and learn their spatial- and channel interactions with the shared image tokens in each transformer layer with attention mechanism, as aggregating spatial and channel information is critical for dense prediction tasks. Each task prompt learns task-specific representation for one task, while all the prompts can jointly contribute to the learning of the shared image token representations, and the interactions between different task prompts model the cross-task relationship. To decode dense predictions for multiple tasks with the learned spatial-channel task prompts from transformer, we accordingly design a dense task prompt decoding mechanism, which queries the shared image tokens using task prompts to obtain spatial- and channel-wise task-specific representations. Extensive experiments on two challenging multi-task dense scene understanding benchmarks (i.e. NYUD-V2 and PASCAL-Context) show the superiority of the proposed framework and TaskPrompter establishes significant state-of-the-art performances on multi-task dense predictions. Codes and models are made publicly available at https://github.com/prismformore/Multi-Task-Transformer. | https://openreview.net/pdf/ebe8ec2526826ab8aca7189c0b3935036603f11c.pdf |
Learning Domain-Agnostic Representation for Disease Diagnosis | https://openreview.net/forum?id=-HHJZlRpGb | https://openreview.net/forum?id=-HHJZlRpGb | Churan Wang,Jing Li,Xinwei Sun,Fandong Zhang,Yizhou Yu,Yizhou Wang | ICLR 2023,Poster | In clinical environments, image-based diagnosis is desired to achieve robustness on multi-center samples. Toward this goal, a natural way is to capture only clinically disease-related features. However, such disease-related features are often entangled with center-effect, disabling robust transferring to unseen centers/domains. To disentangle disease-related features, we first leverage structural causal modeling to explicitly model disease-related and center-effects that are provable to be disentangled from each other. Guided by this, we propose a novel Domain Agnostic Representation Model (DarMo) based on variational Auto-Encoder. To facilitate disentanglement, we design domain-agnostic and domain-aware encoders to respectively capture disease-related features and varied center-effects by incorporating a domain-aware batch normalization layer. Besides, we constrain the disease-related features to well predict the disease label as well as clinical attributes, by leveraging Graph Convolutional Network (GCN) into our decoder. The effectiveness and utility of our method are demonstrated by the superior performance over others on both public datasets and inhouse datasets. | https://openreview.net/pdf/04225040cdc5b044ca6f8a49e174e32a0bff4196.pdf |
Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learning | https://openreview.net/forum?id=JdgO-ht1uTN | https://openreview.net/forum?id=JdgO-ht1uTN | Chi Han,Qizheng He,Charles Yu,Xinya Du,Hanghang Tong,Heng Ji | ICLR 2023,Poster | Probabilistic logical rule learning has shown great strength in logical rule mining and knowledge graph completion. It learns logical rules to predict missing edges by reasoning on existing edges in the knowledge graph. However, previous efforts have largely been limited to only modeling chain-like Horn clauses such as R1(x; z) ^ R2(z; y) ) H(x; y). This formulation overlooks additional contextual information from neighboring sub-graphs of entity variables x, y and z. Intuitively, there is a large gap here, as local sub-graphs have been found to provide important information for knowledge graph completion. Inspired by these observations, we propose Logical Entity RePresentation (LERP) to encode contextual information of entities in the knowledge graph. A LERP is designed as a vector of probabilistic logical functions on the entity’s neighboring sub-graph. It is an interpretable representation while allowing for differentiable optimization. We can then incorporate LERP into probabilistic logical rule learning to learn more expressive rules. Empirical results demonstrate that with LERP, our model outperforms other rule learning methods in knowledge graph completion and is comparable or even superior to state-of-the-art black-box methods. Moreover, we find that our model can discover a more expressive family of logical rules. LERP can also be further combined with embedding learning methods like TransE to make it more interpretable. | https://openreview.net/pdf/7d83ec38f937638fd742f945c7696fb22476dc58.pdf |
BEVDistill: Cross-Modal BEV Distillation for Multi-View 3D Object Detection | https://openreview.net/forum?id=-2zfgNS917 | https://openreview.net/forum?id=-2zfgNS917 | Zehui Chen,Zhenyu Li,Shiquan Zhang,Liangji Fang,Qinhong Jiang,Feng Zhao | ICLR 2023,Poster | 3D object detection from multiple image views is a fundamental and challenging task for visual scene understanding. Owing to its low cost and high efficiency, multi-view 3D object detection has demonstrated promising application prospects. However, accurately detecting objects through perspective views is extremely difficult due to the lack of depth information. Current approaches tend to adopt heavy backbones for image encoders, making them inapplicable for real-world deployment. Different from the images, LiDAR points are superior in providing spatial cues, resulting in highly precise localization. In this paper, we explore the incorporation of LiDAR-based detectors for multi-view 3D object detection. Instead of directly training a depth prediction network, we unify the image and LiDAR features in the Bird-Eye-View (BEV) space and adaptively transfer knowledge across non-homogenous representations in a teacher-student paradigm. To this end, we propose BEVDistill, a cross-modal BEV knowledge distillation (KD) framework for multi-view 3D object detection.
Extensive experiments demonstrate that the proposed method outperforms current KD approaches on a highly-competitive baseline, BEVFormer, without introducing any extra cost in the inference phase. Notably, our best model achieves 59.4 NDS on the nuScenes test leaderboard, achieving new state-of-the-arts in comparison with various image-based detectors. Code will be available at https://github.com/zehuichen123/BEVDistill. | https://openreview.net/pdf/675b1bf3a47333e985a80f97d69ed4203c3f3ba1.pdf |
A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification | https://openreview.net/forum?id=MLJ5TF5FtXH | https://openreview.net/forum?id=MLJ5TF5FtXH | Xiang Hu,XinYu KONG,Kewei Tu | ICLR 2023,Poster | Deep neural networks based on layer-stacking architectures have historically suffered from poor inherent interpretability. Meanwhile, symbolic probabilistic models function with clear interpretability, but how to combine them with neural networks to enhance their performance remains to be explored. In this paper, we try to marry these two systems for text classification via a structured language model. We propose a Symbolic-Neural model that can learn to explicitly predict class labels of text spans from a constituency tree without requiring any access to span-level gold labels. As the structured language model learns to predict constituency trees in a self-supervised manner, only raw texts and sentence-level labels are required as training data, which makes it essentially a general constituent-level self-interpretable classification model. Our experiments demonstrate that our approach could achieve good prediction accuracy in downstream tasks. Meanwhile, the predicted span labels are consistent with human rationales to a certain degree. | https://openreview.net/pdf/61a0f38810f4448bf5fb2a81d922d715f92960d9.pdf |
Suppressing the Heterogeneity: A Strong Feature Extractor for Few-shot Segmentation | https://openreview.net/forum?id=CGuvK3U09LH | https://openreview.net/forum?id=CGuvK3U09LH | Zhengdong Hu,Yifan Sun,Yi Yang | ICLR 2023,Poster | This paper tackles the Few-shot Semantic Segmentation (FSS) task with focus on learning the feature extractor. Somehow the feature extractor has been overlooked by recent state-of-the-art methods, which directly use a deep model pretrained on ImageNet for feature extraction (without further fine-tuning). Under this background, we think the FSS feature extractor deserves exploration and observe the heterogeneity (i.e., the intra-class diversity in the raw images) as a critical challenge hindering the intra-class feature compactness. The heterogeneity has three levels from coarse to fine: 1) Sample-level: the inevitable distribution gap between the support and query images makes them heterogeneous from each other. 2) Region-level: the background in FSS actually contains multiple regions with different semantics. 3) Patch-level: some neighboring patches belonging to a same class may appear quite different from each other. Motivated by these observations, we propose a feature extractor with Multi-level Heterogeneity Suppressing (MuHS). MuHS leverages the attention mechanism in transformer backbone to effectively suppress all these three-level heterogeneity. Concretely, MuHS reinforces the attention / interaction between different samples (query and support), different regions and neighboring patches by constructing cross-sample attention, cross-region interaction and a novel masked image segmentation (inspired by the recent masked image modeling), respectively. We empirically show that 1) MuHS brings consistent improvement for various FSS heads and 2) using a simple linear classification head, MuHS sets new states of the art on multiple FSS datasets, validating the importance of FSS feature learning. | https://openreview.net/pdf/41a97d1a38e6c62bc037998a39c3201184ec1317.pdf |
Achieve the Minimum Width of Neural Networks for Universal Approximation | https://openreview.net/forum?id=hfUJ4ShyDEU | https://openreview.net/forum?id=hfUJ4ShyDEU | Yongqiang Cai | ICLR 2023,Poster | The universal approximation property (UAP) of neural networks is fundamental for deep learning, and it is well known that wide neural networks are universal approximators of continuous functions within both the $L^p$ norm and the continuous/uniform norm. However, the exact minimum width, $w_{\min}$, for the UAP has not been studied thoroughly. Recently, using a decoder-memorizer-encoder scheme, \citet{Park2021Minimum} found that $w_{\min} = \max(d_x+1,d_y)$ for both the $L^p$-UAP of ReLU networks and the $C$-UAP of ReLU+STEP networks, where $d_x,d_y$ are the input and output dimensions, respectively. In this paper, we consider neural networks with an arbitrary set of activation functions. We prove that both $C$-UAP and $L^p$-UAP for functions on compact domains share a universal lower bound of the minimal width; that is, $w^*_{\min} = \max(d_x,d_y)$. In particular, the critical width, $w^*_{\min}$, for $L^p$-UAP can be achieved by leaky-ReLU networks, provided that the input or output dimension is larger than one. Our construction is based on the approximation power of neural ordinary differential equations and the ability to approximate flow maps by neural networks. The nonmonotone or discontinuous activation functions case and the one-dimensional case are also discussed. | https://openreview.net/pdf/ba2d19bc5e6a62f1fb01d4bd5f2e73fabbc2a98e.pdf |
H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection | https://openreview.net/forum?id=NPfDKT9OUJ3 | https://openreview.net/forum?id=NPfDKT9OUJ3 | Xue Yang,Gefan Zhang,Wentong Li,Yue Zhou,Xuehui Wang,Junchi Yan | ICLR 2023,Poster | Oriented object detection emerges in many applications from aerial images to autonomous driving, while many existing detection benchmarks are annotated with horizontal bounding box only which is also less costive than fine-grained rotated box, leading to a gap between the readily available training corpus and the rising demand for oriented object detection. This paper proposes a simple yet effective oriented object detection approach called H2RBox merely using horizontal box annotation for weakly-supervised training, which closes the above gap and shows competitive performance even against those trained with rotated boxes. The cores of our method are weakly- and self-supervised learning, which predicts the angle of the object by learning the consistency of two different views. To our best knowledge, H2RBox is the first horizontal box annotation-based oriented object detector. Compared to an alternative i.e. horizontal box-supervised instance segmentation with our post adaption to oriented object detection, our approach is not susceptible to the prediction quality of mask and can perform more robustly in complex scenes containing a large number of dense objects and outliers. Experimental results show that H2RBox has significant performance and speed advantages over horizontal box-supervised instance segmentation methods, as well as lower memory requirements. While compared to rotated box-supervised oriented object detectors, our method shows very close performance and speed. The source code is available at PyTorch-based \href{https://github.com/yangxue0827/h2rbox-mmrotate}{MMRotate} and Jittor-based \href{https://github.com/yangxue0827/h2rbox-jittor}{JDet}. | https://openreview.net/pdf/9bb307095a44559ad6b55f315ec3bf57d439a0bc.pdf |
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore | https://openreview.net/forum?id=xzmqxHdZAwO | https://openreview.net/forum?id=xzmqxHdZAwO | Guoyang Xie,Jinbao Wang,Jiaqi Liu,Yaochu Jin,Feng Zheng | ICLR 2023,Poster | In the area of few-shot anomaly detection (FSAD), efficient visual feature plays an essential role in the memory bank $\mathcal{M}$-based methods. However, these methods do not account for the relationship between the visual feature and its rotated visual feature, drastically limiting the anomaly detection performance. To push the limits, we reveal that rotation-invariant feature property has a significant impact on industrial-based FSAD. Specifically, we utilize graph representation in FSAD and provide a novel visual isometric invariant feature (VIIF) as an anomaly measurement feature. As a result, VIIF can robustly improve the anomaly discriminating ability and can further reduce the size of redundant features stored in $\mathcal{M}$ by a large amount. Besides, we provide a novel model GraphCore via VIIFs that can fast implement unsupervised FSAD training and improve the performance of anomaly detection. A comprehensive evaluation is provided for comparing GraphCore and other SOTA anomaly detection models under our proposed few-shot anomaly detection setting, which shows GraphCore can increase average AUC by 5.8%, 4.1%, 3.4%, and 1.6% on MVTec AD and by 25.5%, 22.0%, 16.9%, and 14.1% on MPDD for 1, 2, 4, and 8-shot cases, respectively. | https://openreview.net/pdf/f976a1589f4738386a65f414cce8966e059b37a9.pdf |
Representation Learning for Low-rank General-sum Markov Games | https://openreview.net/forum?id=8FroynZv4C | https://openreview.net/forum?id=8FroynZv4C | Chengzhuo Ni,Yuda Song,Xuezhou Zhang,Zihan Ding,Chi Jin,Mengdi Wang | ICLR 2023,Poster | We study multi-agent general-sum Markov games with nonlinear function approximation. We focus on low-rank Markov games whose transition matrix admits a hidden low-rank structure on top of an unknown non-linear representation. The goal is to design an algorithm that (1) finds an $\varepsilon$-equilibrium policy sample efficiently without prior knowledge of the environment or the representation, and (2) permits a deep-learning friendly implementation. We leverage representation learning and present a model-based and a model-free approach to construct an effective representation from collected data. For both approaches, the algorithm achieves a sample complexity of poly$(H,d,A,1/\varepsilon)$, where $H$ is the game horizon, $d$ is the dimension of the feature vector, $A$ is the size of the joint action space and $\varepsilon$ is the optimality gap. When the number of players is large, the above sample complexity can scale exponentially with the number of players in the worst case. To address this challenge, we consider Markov Games with a factorized transition structure and present an algorithm that escapes such exponential scaling. To our best knowledge, this is the first sample-efficient algorithm for multi-agent general-sum Markov games that incorporates (non-linear) function approximation. We accompany our theoretical result with a neural network-based implementation of our algorithm and evaluate it against the widely used deep RL baseline, DQN with fictitious play. | https://openreview.net/pdf/e4a6700bd0867d75ca3bdb47aa252ba2425e580c.pdf |
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts | https://openreview.net/forum?id=APuPRxjHvZ | https://openreview.net/forum?id=APuPRxjHvZ | Yoonho Lee,Annie S Chen,Fahim Tajwar,Ananya Kumar,Huaxiu Yao,Percy Liang,Chelsea Finn | ICLR 2023,Poster | A common approach to transfer learning under distribution shift is to fine-tune the last few layers of a pre-trained model, preserving learned features while also adapting to the new task. This paper shows that in such settings, selectively fine-tuning a subset of layers (which we term surgical fine-tuning) matches or outperforms commonly used fine-tuning approaches. Moreover, the type of distribution shift influences which subset is more effective to tune: for example, for image corruptions, fine-tuning only the first few layers works best. We validate our findings systematically across seven real-world data tasks spanning three types of distribution shifts. Theoretically, we prove that for two-layer neural networks in an idealized setting, first-layer tuning can outperform fine-tuning all layers. Intuitively, fine-tuning more parameters on a small target dataset can cause information learned during pre-training to be forgotten, and the relevant information depends on the type of shift. | https://openreview.net/pdf/8a90aaabfc83b6bbb06e9e18b8e9657a7615415d.pdf |
Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement | https://openreview.net/forum?id=RVTOp3MwT3n | https://openreview.net/forum?id=RVTOp3MwT3n | Yoonho Lee,Huaxiu Yao,Chelsea Finn | ICLR 2023,Poster | Real-world machine learning problems often exhibit shifts between the source and target distributions, in which source data does not fully convey the desired behavior on target inputs. Different functions that achieve near-perfect source accuracy can make differing predictions on test inputs, and such ambiguity makes robustness to distribution shifts challenging. We propose DivDis, a simple two-stage framework for identifying and resolving ambiguity in data. DivDis first learns a diverse set of hypotheses that achieve low source loss but make differing predictions on target inputs. We then disambiguate by selecting one of the discovered functions using additional information, for example, a small number of target labels. Our experimental evaluation shows improved performance in subpopulation shift and domain generalization settings, demonstrating that DivDis can scalably adapt to distribution shifts in image and text classification benchmarks. | https://openreview.net/pdf/129fbcfd8020755648ecd2355fbd7899e2e45c08.pdf |
On amortizing convex conjugates for optimal transport | https://openreview.net/forum?id=TQ5WUwS_4ai | https://openreview.net/forum?id=TQ5WUwS_4ai | Brandon Amos | ICLR 2023,Poster | This paper focuses on computing the convex conjugate operation that arises when solving Euclidean Wasserstein-2 optimal transport problems. This conjugation, which is also referred to as the Legendre-Fenchel conjugate or c-transform,is considered difficult to compute and in practice,Wasserstein-2 methods are limited by not being able to exactly conjugate the dual potentials in continuous space. To overcome this, the computation of the conjugate can be approximated with amortized optimization, which learns a model to predict the conjugate. I show that combining amortized approximations to the conjugate with a solver for fine-tuning significantly improves the quality of transport maps learned for the Wasserstein-2 benchmark by Korotin et al. (2021a) and is able to model many 2-dimensional couplings and flows considered in the literature. All of the baselines, methods, and solvers in this paper are available at http://github.com/facebookresearch/w2ot. | https://openreview.net/pdf/24dd9f66ee9d2c057f311ec049f472bba267266c.pdf |
DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation | https://openreview.net/forum?id=I_YZANaz5X | https://openreview.net/forum?id=I_YZANaz5X | Yan Zhao,Ruihai Wu,Zhehuan Chen,Yourong Zhang,Qingnan Fan,Kaichun Mo,Hao Dong | ICLR 2023,Poster | It is essential yet challenging for future home-assistant robots to understand and manipulate diverse 3D objects in daily human environments. Towards building scalable systems that can perform diverse manipulation tasks over various 3D shapes, recent works have advocated and demonstrated promising results learning visual actionable affordance, which labels every point over the input 3D geometry with an action likelihood of accomplishing the downstream task (e.g., pushing or picking-up). However, these works only studied single-gripper manipulation tasks, yet many real-world tasks require two hands to achieve collaboratively. In this work, we propose a novel learning framework, DualAfford, to learn collaborative affordance for dual-gripper manipulation tasks. The core design of the approach is to reduce the quadratic problem for two grippers into two disentangled yet interconnected subtasks for efficient learning. Using the large-scale PartNet-Mobility and ShapeNet datasets, we set up four benchmark tasks for dual-gripper manipulation. Experiments prove the effectiveness and superiority of our method over three baselines. We will release code and data upon acceptance. | https://openreview.net/pdf/3d748072189f631dccc0d79e113d3909f32f78b5.pdf |
Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching | https://openreview.net/forum?id=CjTHVo1dvR | https://openreview.net/forum?id=CjTHVo1dvR | Shengchao Liu,Hongyu Guo,Jian Tang | ICLR 2023,Poster | Molecular representation pretraining is critical in various applications for drug and material discovery due to the limited number of labeled molecules, and most existing work focuses on pretraining on 2D molecular graphs. However, the power of pretraining on 3D geometric structures has been less explored. This is owing to the difficulty of finding a sufficient proxy task that can empower the pretraining to effectively extract essential features from the geometric structures. Motivated by the dynamic nature of 3D molecules, where the continuous motion of a molecule in the 3D Euclidean space forms a smooth potential energy surface, we propose GeoSSL, a 3D coordinate denoising pretraining framework to model such an energy landscape. Further by leveraging an SE(3)-invariant score matching method, we propose GeoSSL-DDM in which the coordinate denoising proxy task is effectively boiled down to denoising the pairwise atomic distances in a molecule. Our comprehensive experiments confirm the effectiveness and robustness of our proposed method. | https://openreview.net/pdf/9c1d1ecbf17ead138572cf85c7ea92842f80fafc.pdf |
SIMPLE: Specialized Model-Sample Matching for Domain Generalization | https://openreview.net/forum?id=BqrPeZ_e5P | https://openreview.net/forum?id=BqrPeZ_e5P | Ziyue Li,Kan Ren,XINYANG JIANG,Yifei Shen,Haipeng Zhang,Dongsheng Li | ICLR 2023,Poster | In domain generalization (DG), most existing methods aspire to fine-tune a specific pretrained model through novel DG algorithms. In this paper, we propose an alternative direction, i.e., to efficiently leverage a pool of pretrained models without fine-tuning. Through extensive empirical and theoretical evidence, we demonstrate that (1) pretrained models have possessed generalization to some extent while there is no single best pretrained model across all distribution shifts, and (2) out-of-distribution (OOD) generalization error depends on the fitness between the pretrained model and unseen test distributions. This analysis motivates us to incorporate diverse pretrained models and to dispatch the best matched models for each OOD sample by means of recommendation techniques. To this end, we propose SIMPLE, a specialized model-sample matching method for domain generalization. First, the predictions of pretrained models are adapted to the target domain by a linear label space transformation. A matching network aware of model specialty is then proposed to dynamically recommend proper pretrained models to predict each test sample. The experiments on DomainBed show that our method achieves significant performance improvements (up to 12.2% for individual dataset and 3.9% on average) compared to state-of-the-art (SOTA) methods and further achieves 6.1% gain via enlarging the pretrained model pool. Moreover, our method is highly efficient and achieves more than 1000 times training speedup compared to the conventional DG methods with fine-tuning a pretrained model. Code and supplemental materials are available at https://seqml.github.io/simple. | https://openreview.net/pdf/6b566de37f3281e3a1bec3d36af62f11b72f4fc5.pdf |
The Augmented Image Prior: Distilling 1000 Classes by Extrapolating from a Single Image | https://openreview.net/forum?id=6kxApT2r2i | https://openreview.net/forum?id=6kxApT2r2i | Yuki M Asano,Aaqib Saeed | ICLR 2023,Poster | What can neural networks learn about the visual world when provided with only a single image as input? While any image obviously cannot contain the multitudes of all existing objects, scenes and lighting conditions -- within the space of all $256^{3\cdot224\cdot224}$ possible $224$-sized square images, it might still provide a strong prior for natural images. To analyze this ``augmented image prior'' hypothesis, we develop a simple framework for training neural networks from scratch using a single image and augmentations using knowledge distillation from a supervised pretrained teacher. With this, we find the answer to the above question to be: `surprisingly, a lot'. In quantitative terms, we find accuracies of $94\%$/$74\%$ on CIFAR-10/100, $69$\% on ImageNet, and by extending this method to video and audio, $51\%$ on Kinetics-400 and $84$\% on SpeechCommands. In extensive analyses spanning 13 datasets, we disentangle the effect of augmentations, choice of data and network architectures and also provide qualitative evaluations that include lucid ``panda neurons'' in networks that have never even seen one. | https://openreview.net/pdf/50fe7bfa8213c9e0ffde22b5d39aec2fb7c77b89.pdf |
Delving into Semantic Scale Imbalance | https://openreview.net/forum?id=07tc5kKRIo | https://openreview.net/forum?id=07tc5kKRIo | Yanbiao Ma,Licheng Jiao,Fang Liu,Yuxin Li,Shuyuan Yang,Xu Liu | ICLR 2023,Poster | Model bias triggered by long-tailed data has been widely studied. However, measure based on the number of samples cannot explicate three phenomena simultaneously: (1) Given enough data, the classification performance gain is marginal with additional samples. (2) Classification performance decays precipitously as the number of training samples decreases when there is insufficient data. (3) Model trained on sample-balanced datasets still has different biases for different classes. In this work, we define and quantify the semantic scale of classes, which is equivalent to the feature diversity of classes. It is exciting to find experimentally that there is a marginal effect of semantic scale, which perfectly describes the first two phenomena. Further, the quantitative measurement of semantic scale imbalance is proposed, which can accurately reflect model bias on multiple datasets, even on sample-balanced data, revealing a novel perspective for the study of class imbalance. Due to the prevalence of semantic scale imbalance, we propose semantic-scale-balanced learning, including a general loss improvement scheme and a dynamic re-weighting training framework that overcomes the challenge of calculating semantic scales in real-time during iterations. Comprehensive experiments show that dynamic semantic-scale-balanced learning consistently enables the model to perform superiorly on large-scale long-tailed and non-long-tailed datasets, which is a good starting point for mitigating the prevalent but unnoticed model bias. | https://openreview.net/pdf/9a6bf52f5fc27a9256d13652e759f11fd7e78f1b.pdf |
DAG Matters! GFlowNets Enhanced Explainer for Graph Neural Networks | https://openreview.net/forum?id=jgmuRzM-sb6 | https://openreview.net/forum?id=jgmuRzM-sb6 | Wenqian Li,Yinchuan Li,Zhigang Li,Jianye HAO,Yan Pang | ICLR 2023,Poster | Uncovering rationales behind predictions of graph neural networks (GNNs) has received increasing attention over the years. Existing literature mainly focus on selecting a subgraph, through combinatorial optimization, to provide faithful explanations. However, the exponential size of candidate subgraphs limits the applicability of state-of-the-art methods to large-scale GNNs. We enhance on this through a different approach: by proposing a generative structure – GFlowNets-based GNN Explainer (GFlowExplainer), we turn the optimization problem into a step-by-step generative problem. Our GFlowExplainer aims to learn a policy that generates a distribution of subgraphs for which the probability of a subgraph is proportional to its’ reward. The proposed approach eliminates the influence of node sequence and thus does not need any pre-training strategies. We also propose a new cut vertex matrix to efficiently explore parent states for GFlowNets structure, thus making our approach applicable in a large-scale setting. We conduct extensive experiments on both synthetic and real datasets, and both qualitative and quantitative results show the superiority of our GFlowExplainer. | https://openreview.net/pdf/8a760853df1498c71ec2c328ac4842a2fcba52ee.pdf |
Contextual Image Masking Modeling via Synergized Contrasting without View Augmentation for Faster and Better Visual Pretraining | https://openreview.net/forum?id=A3sgyt4HWp | https://openreview.net/forum?id=A3sgyt4HWp | Shaofeng Zhang,Feng Zhu,Rui Zhao,Junchi Yan | ICLR 2023,Poster | We propose a new contextual masking image modeling (MIM) approach called contrasting-aided contextual MIM (ccMIM), under the MIM paradigm for visual pretraining. Specifically, we adopt importance sampling to select the masked patches with richer semantic information for reconstruction, instead of random sampling as done in previous MIM works. As such, the resulting patch reconstruction task from the remaining less semantic patches could be more difficult and helps to learn. To speed up the possibly slowed convergence due to our more difficult reconstruction task, we further propose a new contrastive loss that aligns the tokens of the vision transformer extracted from the selected masked patches and the remaining ones, respectively. The hope is that it serves as a regularizer for patch feature learning such that the image-level global information could be captured in both masked and unmasked patches, and notably such a single-view contrasting avoids the tedious image augmentation step required in recent efforts of introducing contrastive learning to MIM (to speedup convergence and discriminative ability). Meanwhile, the attention score from the contrastive global feature can also carry effective semantic clues to in turn guide our above masking patch selection scheme. In consequence, our contextual MIM and contrastive learning are synergetically performed in a loop (semantic patch selection-token alignment contrasting) to boost the best of the two worlds: fast convergence and strong performance on downstream tasks without ad-hoc augmentations, which are verified by empirical results on ImageNet-1K for both classification and dense vision tasks. | https://openreview.net/pdf/ee495118220964827cb6952298385c24d0aadbb8.pdf |
Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning | https://openreview.net/forum?id=10R_bcjFwJ | https://openreview.net/forum?id=10R_bcjFwJ | Shaofeng Zhang,Feng Zhu,Rui Zhao,Junchi Yan | ICLR 2023,Poster | We propose ADCLR: \underline{A}ccurate and \underline{D}ense \underline{C}ontrastive \underline{R}epresentation \underline{L}earning, a novel self-supervised learning framework for learning accurate and dense vision representation. To extract spatial-sensitive information, ADCLR introduces query patches for contrasting in addition with global contrasting. Compared with previous dense contrasting methods, ADCLR mainly enjoys three merits: i) achieving both global-discriminative and spatial-sensitive representation, ii) model-efficient (no extra parameters in addition to the global contrasting baseline), and iii) correspondence-free and thus simpler to implement. Our approach achieves new state-of-the-art performance for contrastive methods. On classification tasks, for ViT-S, ADCLR achieves 78.1\% top-1 accuracy on ImageNet with linear probing, outperforming our baseline (DINO) without our devised techniques as plug-in, by 1.1\%. For ViT-B, ADCLR achieves 79.8\%, 84.0\% accuracy on ImageNet by linear probing and finetune, outperforming DINO by 0.6\%, 0.4\% accuracy. For dense tasks, on MS-COCO, ADCLR achieves significant improvements of 44.3\% AP on object detection, 39.7\% AP on instance segmentation, outperforming previous SOTA method SelfPatch by 2.2\% and 1.2\%, respectively. On ADE20K, ADCLR outperforms SelfPatch by 1.0\% mIoU, 1.2\% mAcc on the segmentation task. | https://openreview.net/pdf/2827ac68856f272d96ac55b06f6692f5bd6b1eff.pdf |
Continuous-Discrete Convolution for Geometry-Sequence Modeling in Proteins | https://openreview.net/forum?id=P5Z-Zl9XJ7 | https://openreview.net/forum?id=P5Z-Zl9XJ7 | Hehe Fan,Zhangyang Wang,Yi Yang,Mohan Kankanhalli | ICLR 2023,Poster | The structure of proteins involves 3D geometry of amino acid coordinates and 1D sequence of peptide chains. The 3D structure exhibits irregularity because amino acids are distributed unevenly in Euclidean space and their coordinates are continuous variables. In contrast, the 1D structure is regular because amino acids are arranged uniformly in the chains and their sequential positions (orders) are discrete variables. Moreover, geometric coordinates and sequential orders are in two types of spaces and their units of length are incompatible. These inconsistencies make it challenging to capture the 3D and 1D structures while avoiding the impact of sequence and geometry modeling on each other. This paper proposes a Continuous-Discrete Convolution (CDConv) that uses irregular and regular approaches to model the geometry and sequence structures, respectively. Specifically, CDConv employs independent learnable weights for different regular sequential displacements but directly encodes geometric displacements due to their irregularity. In this way, CDConv significantly improves protein modeling by reducing the impact of geometric irregularity on sequence modeling. Extensive experiments on a range of tasks, including protein fold classification, enzyme reaction classification, gene ontology term prediction and enzyme commission number prediction, demonstrate the effectiveness of the proposed CDConv. | https://openreview.net/pdf/df7205cde5aafbeeadb773e78c03a4eb6be38ef0.pdf |
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