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VoGE: A Differentiable Volume Renderer using Gaussian Ellipsoids for Analysis-by-Synthesis | https://openreview.net/forum?id=AdPJb9cud_Y | https://openreview.net/forum?id=AdPJb9cud_Y | Angtian Wang,Peng Wang,Jian Sun,Adam Kortylewski,Alan Yuille | ICLR 2023,Poster | Differentiable rendering allows the application of computer graphics on vision tasks, e.g. object pose and shape fitting, via analysis-by-synthesis, where gradients at occluded regions are important when inverting the rendering process.To obtain those gradients, state-of-the-art (SoTA) differentiable renderers use rasterization to collect a set of nearest components for each pixel and aggregate them based on the viewing distance. In this paper, we propose VoGE, which uses ray tracing to capture nearest components with their volume density distributions on the rays and aggregates via integral of the volume densities based on Gaussian ellipsoids, which brings more efficient and stable gradients. To efficiently render via VoGE, we propose an approximate close-form solution for the volume density aggregation and a coarse-to-fine rendering strategy. Finally, we provide a CUDA implementation of VoGE, which gives a competitive rendering speed in comparison to PyTorch3D. Quantitative and qualitative experiment results show VoGE outperforms SoTA counterparts when applied to various vision tasks, e.g., object pose estimation, shape/texture fitting, and occlusion reasoning. The VoGE code is available at: https://github.com/Angtian/VoGE. | https://openreview.net/pdf/12db575ff699c124b43732feb8429167604a128c.pdf |
The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers | https://openreview.net/forum?id=TJ2nxciYCk- | https://openreview.net/forum?id=TJ2nxciYCk- | Zonglin Li,Chong You,Srinadh Bhojanapalli,Daliang Li,Ankit Singh Rawat,Sashank J. Reddi,Ke Ye,Felix Chern,Felix Yu,Ruiqi Guo,Sanjiv Kumar | ICLR 2023,Poster | This paper studies a curious phenomenon that machine learning model with Transformer architectures have sparse activation maps. By activation map we refer to the intermediate output of the multi-layer perceptrons (MLPs) after a ReLU activation function, and by "sparse" we mean that on average very few entries (e.g., 3.0% for T5-Base and 6.3% for ViT-B16) are nonzero for each input to MLP. Moreover, larger Transformers with more layers and wider MLP hidden dimensions are sparser as measured by the percentage of nonzero entries. Through extensive experiments we demonstrate that the emergence of sparsity is a prevalent phenomenon that occurs for both natural language processing and vision tasks, on both training and evaluation data, for Transformers of various configurations, at layers of all depth levels. We discuss how sparsity immediately implies a way to significantly reduce the FLOP count and improve efficiency for Transformers. Moreover, we demonstrate perhaps surprisingly that enforcing an even sparser activation via Top-k thresholding with a small k brings a collection of desired properties, namely less sensitivity to noisy training data, more robustness to input corruptions, and better calibration for their prediction confidence. | https://openreview.net/pdf/4120f135fba7001a85237ce4a81740c0626abb31.pdf |
FoSR: First-order spectral rewiring for addressing oversquashing in GNNs | https://openreview.net/forum?id=3YjQfCLdrzz | https://openreview.net/forum?id=3YjQfCLdrzz | Kedar Karhadkar,Pradeep Kr. Banerjee,Guido Montufar | ICLR 2023,Poster | Graph neural networks (GNNs) are able to leverage the structure of graph data by passing messages along the edges of the graph. While this allows GNNs to learn features depending on the graph structure, for certain graph topologies it leads to inefficient information propagation and a problem known as oversquashing. This has recently been linked with the curvature and spectral gap of the graph. On the other hand, adding edges to the message-passing graph can lead to increasingly similar node representations and a problem known as oversmoothing. We propose a computationally efficient algorithm that prevents oversquashing by systematically adding edges to the graph based on spectral expansion. We combine this with a relational architecture, which lets the GNN preserve the original graph structure and provably prevents oversmoothing. We find experimentally that our algorithm outperforms existing graph rewiring methods in several graph classification tasks. | https://openreview.net/pdf/ab8a3a426c3509fa2d1abd817a2a2b8e7538261c.pdf |
Generative Modeling Helps Weak Supervision (and Vice Versa) | https://openreview.net/forum?id=3OaBBATwsvP | https://openreview.net/forum?id=3OaBBATwsvP | Benedikt Boecking,Nicholas Roberts,Willie Neiswanger,Stefano Ermon,Frederic Sala,Artur Dubrawski | ICLR 2023,Poster | Many promising applications of supervised machine learning face hurdles in the acquisition of labeled data in sufficient quantity and quality, creating an expensive bottleneck. To overcome such limitations, techniques that do not depend on ground truth labels have been studied, including weak supervision and generative modeling. While these techniques would seem to be usable in concert, improving one another, how to build an interface between them is not well-understood. In this work, we propose a model fusing programmatic weak supervision and generative adversarial networks and provide theoretical justification motivating this fusion. The proposed approach captures discrete latent variables in the data alongside the weak supervision derived label estimate. Alignment of the two allows for better modeling of sample-dependent accuracies of the weak supervision sources, improving the estimate of unobserved labels. It is the first approach to enable data augmentation through weakly supervised synthetic images and pseudolabels. Additionally, its learned latent variables can be inspected qualitatively. The model outperforms baseline weak supervision label models on a number of multiclass image classification datasets, improves the quality of generated images, and further improves end-model performance through data augmentation with synthetic samples. | https://openreview.net/pdf/bb17eb04238e75fcb4c9ceee6174d6c2e0d19afd.pdf |
Provable Memorization Capacity of Transformers | https://openreview.net/forum?id=8JCg5xJCTPR | https://openreview.net/forum?id=8JCg5xJCTPR | Junghwan Kim,Michelle Kim,Barzan Mozafari | ICLR 2023,Poster | Quantifying memorization capacity is essential for understanding the expressiveness and generalizability of deep learning model architectures. However, the memorization capacity of the Transformer architecture has yet to be explored. In this work, we present the first study of the memorization capacity of the Transformer architecture. We prove that Transformers are capable of memorizing $N$ sequence-to-sequence mappings of length $n$ with $d$-dimensional input tokens using $\tilde{O}(d + n + \sqrt{nN})$ parameters. Our theory supports memorization both with and without permutation equivariance, utilizing positional encodings in the latter case. Building on our theory, we also analyze the memorization capacity of Transformers in the sequence classification and language modeling tasks. To verify these theoretical findings, we conduct experiments analyzing the memorization capacity of Transformers in the natural language domain. | https://openreview.net/pdf/210c7ca55ed5081f2330f1d2a10bfa5fef9ed9a5.pdf |
Bridge the Inference Gaps of Neural Processes via Expectation Maximization | https://openreview.net/forum?id=A7v2DqLjZdq | https://openreview.net/forum?id=A7v2DqLjZdq | Qi Wang,Marco Federici,Herke van Hoof | ICLR 2023,Poster | The neural process (NP) is a family of computationally efficient models for learning distributions over functions. However, it suffers from under-fitting and shows suboptimal performance in practice. Researchers have primarily focused on incorporating diverse structural inductive biases, e.g. attention or convolution, in modeling. The topic of inference suboptimality and an analysis of the NP from the optimization objective perspective has hardly been studied in earlier work. To fix this issue, we propose a surrogate objective of the target log-likelihood of the meta dataset within the expectation maximization framework. The resulting model, referred to as the Self-normalized Importance weighted Neural Process (SI-NP), can learn a more accurate functional prior and has an improvement guarantee concerning the target log-likelihood. Experimental results show the competitive performance of SI-NP over other NPs objectives and illustrate that structural inductive biases, such as attention modules, can also augment our method to achieve SOTA performance. | https://openreview.net/pdf/963777f73ae448123ab856d4b0485ebd5419c420.pdf |
Masked Vision and Language Modeling for Multi-modal Representation Learning | https://openreview.net/forum?id=ZhuXksSJYWn | https://openreview.net/forum?id=ZhuXksSJYWn | Gukyeong Kwon,Zhaowei Cai,Avinash Ravichandran,Erhan Bas,Rahul Bhotika,Stefano Soatto | ICLR 2023,Poster | In this paper, we study how to use masked signal modeling in vision and language (V+L) representation learning. Instead of developing masked language modeling (MLM) and masked image modeling (MIM) independently, we propose to build joint masked vision and language modeling, where the masked signal of one modality is reconstructed with the help from another modality. This is motivated by the nature of image-text paired data that both of the image and the text convey almost the same information but in different formats. The masked signal reconstruction of one modality conditioned on another modality can also implicitly learn cross-modal alignment between language tokens and image patches. Our experiments on various V+L tasks show that the proposed method, along with common V+L alignment losses, not only achieves state-of-the-art performance by using a large amount of data but also outperforms the other competitors by a significant margin in the regimes of limited training data. | https://openreview.net/pdf/327b556a6ea9da1009be2554324f7d7f94a99cae.pdf |
Agent-based Graph Neural Networks | https://openreview.net/forum?id=8WTAh0tj2jC | https://openreview.net/forum?id=8WTAh0tj2jC | Karolis Martinkus,Pál András Papp,Benedikt Schesch,Roger Wattenhofer | ICLR 2023,Poster | We present a novel graph neural network we call AgentNet, which is designed specifically for graph-level tasks. AgentNet is inspired by sublinear algorithms, featuring a computational complexity that is independent of the graph size. The architecture of AgentNet differs fundamentally from the architectures of traditional graph neural networks. In AgentNet, some trained \textit{neural agents} intelligently walk the graph, and then collectively decide on the output. We provide an extensive theoretical analysis of AgentNet: We show that the agents can learn to systematically explore their neighborhood and that AgentNet can distinguish some structures that are even indistinguishable by 2-WL. Moreover, AgentNet is able to separate any two graphs which are sufficiently different in terms of subgraphs. We confirm these theoretical results with synthetic experiments on hard-to-distinguish graphs and real-world graph classification tasks. In both cases, we compare favorably not only to standard GNNs but also to computationally more expensive GNN extensions. | https://openreview.net/pdf/a33a54dc51ae12d26281cd196933bb3be33a76f3.pdf |
On the Performance of Temporal Difference Learning With Neural Networks | https://openreview.net/forum?id=6JMXLWX68Kj | https://openreview.net/forum?id=6JMXLWX68Kj | HAOXING TIAN,Ioannis Paschalidis,Alex Olshevsky | ICLR 2023,Poster | Neural Temporal Difference (TD) Learning is an approximate temporal difference method for policy evaluation that uses a neural network for function approximation. Analysis of Neural TD Learning has proven to be challenging. In this paper we provide a convergence analysis of Neural TD Learning with a projection onto $B(\theta_0, \omega)$, a ball of fixed radius $\omega$ around the initial point $\theta_0$. We show an approximation bound of $O(\epsilon + 1/\sqrt{m})$ where $\epsilon$ is the approximation quality of the best neural network in $B(\theta_0, \omega)$ and $m$ is the width of all hidden layers in the network. | https://openreview.net/pdf/5fcb43e6457009730f100f462843e8388d5d4972.pdf |
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation | https://openreview.net/forum?id=b0JxQC7JLWh | https://openreview.net/forum?id=b0JxQC7JLWh | Maksym Yatsura,Kaspar Sakmann,N. Grace Hua,Matthias Hein,Jan Hendrik Metzen | ICLR 2023,Poster | Adversarial patch attacks are an emerging security threat for real world deep learning applications. We present Demasked Smoothing, the first approach (up to our knowledge) to certify the robustness of semantic segmentation models against this threat model. Previous work on certifiably defending against patch attacks has mostly focused on image classification task and often required changes in the model architecture and additional training which is undesirable and computationally expensive. In Demasked Smoothing, any segmentation model can be applied without particular training, fine-tuning, or restriction of the architecture. Using different masking strategies, Demasked Smoothing can be applied both for certified detection and certified recovery. In extensive experiments we show that Demasked Smoothing can on average certify 63% of the pixel predictions for a 1% patch in the detection task and 46% against a 0.5% patch for the recovery task on the ADE20K dataset. | https://openreview.net/pdf/33118a52ee49a22c9c466c179d0475d60c082e6c.pdf |
Markup-to-Image Diffusion Models with Scheduled Sampling | https://openreview.net/forum?id=81VJDmOE2ol | https://openreview.net/forum?id=81VJDmOE2ol | Yuntian Deng,Noriyuki Kojima,Alexander M Rush | ICLR 2023,Poster | Building on recent advances in image generation, we present a fully data-driven approach to rendering markup into images. The approach is based on diffusion models, which parameterize the distribution of data using a sequence of denoising operations on top of a Gaussian noise distribution. We view the diffusion denoising process a sequential decision making process, and show that it exhibits compounding errors similar to exposure bias issues in imitation learning problems. To mitigate these issues, we adapt the scheduled sampling algorithm to diffusion training. We conduct experiments on four markup datasets: formulas (LaTeX), table layouts (HTML), sheet music (LilyPond), and molecular images (SMILES). These experiments each verify the effectiveness of diffusion and the use of scheduled sampling to fix generation issues. These results also show that the markup-to-image task presents a useful controlled compositional setting for diagnosing and analyzing generative image models. | https://openreview.net/pdf/f90b1cecbec20cb712499f3efdd915bf72fe7839.pdf |
How Much Space Has Been Explored? Measuring the Chemical Space Covered by Databases and Machine-Generated Molecules | https://openreview.net/forum?id=Yo06F8kfMa1 | https://openreview.net/forum?id=Yo06F8kfMa1 | Yutong Xie,Ziqiao Xu,Jiaqi Ma,Qiaozhu Mei | ICLR 2023,Poster | Forming a molecular candidate set that contains a wide range of potentially effective compounds is crucial to the success of drug discovery. While most databases and machine-learning-based generation models aim to optimize particular chemical properties, there is limited literature on how to properly measure the coverage of the chemical space by those candidates included or generated. This problem is challenging due to the lack of formal criteria to select good measures of the chemical space. In this paper, we propose a novel evaluation framework for measures of the chemical space based on two analyses: an axiomatic analysis with three intuitive axioms that a good measure should obey, and an empirical analysis on the correlation between a measure and a proxy gold standard. Using this framework, we are able to identify #Circles, a new measure of chemical space coverage, which is superior to existing measures both analytically and empirically. We further evaluate how well the existing databases and generation models cover the chemical space in terms of #Circles. The results suggest that many generation models fail to explore a larger space over existing databases, which leads to new opportunities for improving generation models by encouraging exploration.
| https://openreview.net/pdf/22143e65729a6950f65c1f05020b2dca91f0aa53.pdf |
Understanding new tasks through the lens of training data via exponential tilting | https://openreview.net/forum?id=DBMttEEoLbw | https://openreview.net/forum?id=DBMttEEoLbw | Subha Maity,Mikhail Yurochkin,Moulinath Banerjee,Yuekai Sun | ICLR 2023,Poster | Deploying machine learning models on new tasks is a major challenge due to differences in distributions of the train (source) data and the new (target) data. However, the training data likely captures some of the properties of the new task. We consider the problem of reweighing the training samples to gain insights into the distribution of the target task. Specifically, we formulate a distribution shift model based on the exponential tilt assumption and learn train data importance weights minimizing the KL divergence between labeled train and unlabeled target datasets. The learned train data weights can then be used for downstream tasks such as target performance evaluation, fine-tuning, and model selection. We demonstrate the efficacy of our method on Waterbirds and Breeds benchmarks. | https://openreview.net/pdf/30408afe2b1120d0a3085f6b35df853174c6ac8f.pdf |
Calibrating Sequence likelihood Improves Conditional Language Generation | https://openreview.net/forum?id=0qSOodKmJaN | https://openreview.net/forum?id=0qSOodKmJaN | Yao Zhao,Mikhail Khalman,Rishabh Joshi,Shashi Narayan,Mohammad Saleh,Peter J Liu | ICLR 2023,Poster | Conditional language models are predominantly trained with maximum likelihood estimation (MLE), giving probability mass to sparsely observed target sequences. While MLE trained models assign high probability to plausible sequences given the context, the model probabilities often do not accurately rank-order generated sequences by quality. This has been empirically observed in beam search decoding as output quality degrading with large beam sizes, and decoding strategies benefiting from heuristics such as length normalization and repetition-blocking. In this work, we introduce sequence likelihood calibration (SLiC) where the likelihood of model generated sequences are calibrated to better align with reference sequences in the model’s latent space. With SLiC, decoding heuristics become unnecessary and decoding candidates’ quality significantly improves regardless of the decoding method. Furthermore, SLiC shows no sign of diminishing returns with model scale, and presents alternative ways to improve quality with limited training and inference budgets. With SLiC, we exceed or match SOTA results on a wide range of generation tasks spanning abstractive summarization, question generation, abstractive question answering and data-to-text generation, even with modest-sized models. | https://openreview.net/pdf/dfaa19d042f152ef2bcc2b1feef6fb0276c42113.pdf |
Learning differentiable solvers for systems with hard constraints | https://openreview.net/forum?id=vdv6CmGksr0 | https://openreview.net/forum?id=vdv6CmGksr0 | Geoffrey Négiar,Michael W. Mahoney,Aditi Krishnapriyan | ICLR 2023,Poster | We introduce a practical method to enforce partial differential equation (PDE) constraints for functions defined by neural networks (NNs), with a high degree of accuracy and up to a desired tolerance. We develop a differentiable PDE-constrained layer that can be incorporated into any NN architecture. Our method leverages differentiable optimization and the implicit function theorem to effectively enforce physical constraints. Inspired by dictionary learning, our model learns a family of functions, each of which defines a mapping from PDE parameters to PDE solutions. At inference time, the model finds an optimal linear combination of the functions in the learned family by solving a PDE-constrained optimization problem. Our method provides continuous solutions over the domain of interest that accurately satisfy desired physical constraints. Our results show that incorporating hard constraints directly into the NN architecture achieves much lower test error when compared to training on an unconstrained objective. | https://openreview.net/pdf/b2516cea0dddbe611fcfe92c6b78facc1d26b776.pdf |
FedDAR: Federated Domain-Aware Representation Learning | https://openreview.net/forum?id=6P9Y25Pljl6 | https://openreview.net/forum?id=6P9Y25Pljl6 | Aoxiao Zhong,Hao He,Zhaolin Ren,Na Li,Quanzheng Li | ICLR 2023,Poster | Cross-silo Federated learning (FL) has become a promising tool in machine learning applications for healthcare. It allows hospitals/institutions to train models with sufficient data while the data is kept private. To make sure the FL model is robust when facing heterogeneous data among FL clients, most efforts focus on personalizing models for clients. However, the latent relationships between clients' data are ignored. In this work, we focus on a special non-iid FL problem, called Domain-mixed FL, where each client's data distribution is assumed to be a mixture of several predefined domains. Recognizing the diversity of domains and the similarity within domains, we propose a novel method, FedDAR, which learns a domain shared representation and domain-wise personalized prediction heads in a decoupled manner. For simplified linear regression settings, we have theoretically proved that FedDAR enjoys a linear convergence rate. For general settings, we have performed intensive empirical studies on both synthetic and real-world medical datasets which demonstrate its superiority over prior FL methods. Our code is available at https://github.com/zlz0414/FedDAR. | https://openreview.net/pdf/665489145995b4ea0d25bd9c551dc68004d604e5.pdf |
SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models | https://openreview.net/forum?id=TFbwV6I0VLg | https://openreview.net/forum?id=TFbwV6I0VLg | Ziyi Wu,Nikita Dvornik,Klaus Greff,Thomas Kipf,Animesh Garg | ICLR 2023,Poster | Understanding dynamics from visual observations is a challenging problem that requires disentangling individual objects from the scene and learning their interactions. While recent object-centric models can successfully decompose a scene into objects, modeling their dynamics effectively still remains a challenge. We address this problem by introducing SlotFormer -- a Transformer-based autoregressive model operating on learned object-centric representations. Given a video clip, our approach reasons over object features to model spatio-temporal relationships and predicts accurate future object states. In this paper, we successfully apply SlotFormer to perform video prediction on datasets with complex object interactions. Moreover, the unsupervised SlotFormer's dynamics model can be used to improve the performance on supervised downstream tasks, such as Visual Question Answering (VQA), and goal-conditioned planning. Compared to past works on dynamics modeling, our method achieves significantly better long-term synthesis of object dynamics, while retaining high quality visual generation. Besides, SlotFormer enables VQA models to reason about the future without object-level labels, even outperforming counterparts that use ground-truth annotations. Finally, we show its ability to serve as a world model for model-based planning, which is competitive with methods designed specifically for such tasks. | https://openreview.net/pdf/4e21bb00fc659b0459f9ae67df9bb2bec80b42a4.pdf |
Simplifying Model-based RL: Learning Representations, Latent-space Models, and Policies with One Objective | https://openreview.net/forum?id=MQcmfgRxf7a | https://openreview.net/forum?id=MQcmfgRxf7a | Raj Ghugare,Homanga Bharadhwaj,Benjamin Eysenbach,Sergey Levine,Russ Salakhutdinov | ICLR 2023,Poster | While reinforcement learning (RL) methods that learn an internal model of the environment have the potential to be more sample efficient than their model-free counterparts, learning to model raw observations from high dimensional sensors can be challenging.
Prior work has addressed this challenge by learning low-dimensional representation of observations through auxiliary objectives, such as reconstruction or value prediction. However, the alignment between these auxiliary objectives and the RL objective is often unclear.
In this work, we propose a single objective which jointly optimizes a latent-space model and policy to achieve high returns while remaining self-consistent. This objective is a lower bound on expected returns. Unlike prior bounds for model-based RL on policy exploration or model guarantees, our bound is directly on the overall RL objective. We demonstrate that the resulting algorithm matches or improves the sample-efficiency of the best prior model-based and model-free RL methods. While sample efficient methods typically are computationally demanding, our method attains the performance of SAC in about 50\% less wall-clock time.
| https://openreview.net/pdf/f4207005e1b30d58cc78f473150de8c1777a8904.pdf |
Deep Generative Symbolic Regression | https://openreview.net/forum?id=o7koEEMA1bR | https://openreview.net/forum?id=o7koEEMA1bR | Samuel Holt,Zhaozhi Qian,Mihaela van der Schaar | ICLR 2023,Poster | Symbolic regression (SR) aims to discover concise closed-form mathematical equations from data, a task fundamental to scientific discovery. However, the problem is highly challenging because closed-form equations lie in a complex combinatorial search space. Existing methods, ranging from heuristic search to reinforcement learning, fail to scale with the number of input variables. We make the observation that closed-form equations often have structural characteristics and invariances (e.g. the commutative law) that could be further exploited to build more effective symbolic regression solutions. Motivated by this observation, our key contribution is to leverage pre-trained deep generative models to capture the intrinsic regularities of equations, thereby providing a solid foundation for subsequent optimization steps. We show that our novel formalism unifies several prominent approaches of symbolic regression and offers a new perspective to justify and improve on the previous ad hoc designs, such as the usage of cross-entropy loss during pre-training. Specifically, we propose an instantiation of our framework, Deep Generative Symbolic Regression (DGSR). In our experiments, we show that DGSR achieves a higher recovery rate of true equations in the setting of a larger number of input variables, and it is more computationally efficient at inference time than state-of-the-art RL symbolic regression solutions. | https://openreview.net/pdf/82e84acc4e710e5712ae0f5601582769f5bb685c.pdf |
What Can we Learn From The Selective Prediction And Uncertainty Estimation Performance Of 523 Imagenet Classifiers? | https://openreview.net/forum?id=p66AzKi6Xim | https://openreview.net/forum?id=p66AzKi6Xim | Ido Galil,Mohammed Dabbah,Ran El-Yaniv | ICLR 2023,Poster | When deployed for risk-sensitive tasks, deep neural networks must include an uncertainty estimation mechanism.
Here we examine the relationship between deep architectures and their respective training regimes, with their corresponding selective prediction and uncertainty estimation performance. We consider some of the most popular estimation performance metrics previously proposed including AUROC, ECE, AURC as well as coverage for selective accuracy constraint.
We present a novel and comprehensive study of selective prediction and the uncertainty estimation performance of 523 existing pretrained deep ImageNet classifiers that are available in popular repositories.
We identify numerous and previously unknown factors that affect uncertainty estimation and examine the relationships between the different metrics. We find that distillation-based training regimes consistently yield better uncertainty estimations than other training schemes such as vanilla training, pretraining on a larger dataset and adversarial training.
Moreover, we find a subset of ViT models that outperform any other models in terms of uncertainty estimation performance.
For example, we discovered an unprecedented 99% top-1 selective accuracy on ImageNet at 47% coverage
(and 95% top-1 accuracy at 80%) for a ViT model, whereas a competing EfficientNet-V2-XL cannot obtain these accuracy constraints at any level of coverage.
Our companion paper, also published in ICLR 2023 (A framework for benchmarking class-out-of-distribution detection and its application to ImageNet), examines the performance of these classifiers in a class-out-of-distribution setting. | https://openreview.net/pdf/511b590fa52d8862dc31d6bf812574202375626b.pdf |
Predictor-corrector algorithms for stochastic optimization under gradual distribution shift | https://openreview.net/forum?id=2SV2dlfBuE3 | https://openreview.net/forum?id=2SV2dlfBuE3 | Subha Maity,Debarghya Mukherjee,Moulinath Banerjee,Yuekai Sun | ICLR 2023,Poster | Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g., gradual domain shift, object tracking, strategic classification). Often, the underlying process that drives the distribution shift is continuous in nature. We exploit this underlying continuity by developing predictor-corrector algorithms for time-varying stochastic optimization that anticipates changes in the underlying data generating process through a predictor-corrector term in the update rule. The key challenge is the estimation of the predictor-corrector term; a naive approach based on sample-average approximation may lead to non-convergence. We develop a general moving-average based method to estimate the predictor-corrector term and provide error bounds for the iterates, both in presence of pure and noisy access to the queries from the relevant derivatives of the loss function. Furthermore, we show (theoretically and empirically in several examples) that our method outperforms non-predictor corrector methods that do not anticipate changes in the data generating process. | https://openreview.net/pdf/6bd7177a33fed4da74d010246448583573fa2c48.pdf |
AIM: Adapting Image Models for Efficient Video Action Recognition | https://openreview.net/forum?id=CIoSZ_HKHS7 | https://openreview.net/forum?id=CIoSZ_HKHS7 | Taojiannan Yang,Yi Zhu,Yusheng Xie,Aston Zhang,Chen Chen,Mu Li | ICLR 2023,Poster | Recent vision transformer based video models mostly follow the ``image pre-training then finetuning" paradigm and have achieved great success on multiple video benchmarks. However, fully finetuning such a video model could be computationally expensive and unnecessary, given the pre-trained image transformer models have demonstrated exceptional transferability. In this work, we propose a novel method to Adapt pre-trained Image Models (AIM) for efficient video understanding. By freezing the pre-trained image model and adding a few lightweight Adapters, we introduce spatial adaptation, temporal adaptation and joint adaptation to gradually equip an image model with spatiotemporal reasoning capability. We show that our proposed AIM can achieve competitive or even better performance than prior arts with substantially fewer tunable parameters on four video action recognition benchmarks. Thanks to its simplicity, our method is also generally applicable to different image pre-trained models, which has the potential to leverage more powerful image foundation models in the future. The project webpage is https://adapt-image-models.github.io/. | https://openreview.net/pdf/efbcd4004b2689f52afbee57693212e62614552a.pdf |
Impossibly Good Experts and How to Follow Them | https://openreview.net/forum?id=sciA_xgYofB | https://openreview.net/forum?id=sciA_xgYofB | Aaron Walsman,Muru Zhang,Sanjiban Choudhury,Dieter Fox,Ali Farhadi | ICLR 2023,Poster | We consider the sequential decision making problem of learning from an expert that has access to more information than the learner. For many problems this extra information will enable the expert to achieve greater long term reward than any policy without this privileged information access. We call these experts ``Impossibly Good'' because no learning algorithm will be able to reproduce their behavior. However, in these settings it is reasonable to attempt to recover the best policy possible given the agent's restricted access to information. We provide a set of necessary criteria on the expert that will allow a learner to recover the optimal policy in the reduced information space from the expert's advice alone. We also provide a new approach called Elf Distillation (Explorer Learning from Follower) that can be used in cases where these criteria are not met and environmental rewards must be taken into account. We show that this algorithm performs better than a variety of strong baselines on a challenging suite of Minigrid and Vizdoom environments. | https://openreview.net/pdf/b9a9d8247f602605afcc256282e3c3bee4b6efa4.pdf |
Distributionally Robust Post-hoc Classifiers under Prior Shifts | https://openreview.net/forum?id=3KUfbI9_DQE | https://openreview.net/forum?id=3KUfbI9_DQE | Jiaheng Wei,Harikrishna Narasimhan,Ehsan Amid,Wen-Sheng Chu,Yang Liu,Abhishek Kumar | ICLR 2023,Poster | The generalization ability of machine learning models degrades significantly when the test distribution shifts away from the training distribution. We investigate the problem of training models that are robust to shifts caused by changes in the distribution of class-priors or group-priors. The presence of skewed training priors can often lead to the models overfitting to spurious features. Unlike existing methods, which optimize for either the worst or the average performance over classes or groups, our work is motivated by the need for finer control over the robustness properties of the model. We present an extremely lightweight post-hoc approach that performs scaling adjustments to predictions from a pre-trained model, with the goal of minimizing a distributionally robust loss around a chosen target distribution. These adjustments are computed by solving a constrained optimization problem on a validation set and applied to the model during test time. Our constrained optimization objective is inspired from a natural notion of robustness to controlled distribution shifts. Our method comes with provable guarantees and empirically makes a strong case for distributional robust post-hoc classifiers. An empirical implementation is available at https://github.com/weijiaheng/Drops.
| https://openreview.net/pdf/84662db98a6310884aba7817de297b210cdb5c29.pdf |
Transformer Meets Boundary Value Inverse Problems | https://openreview.net/forum?id=HnlCZATopvr | https://openreview.net/forum?id=HnlCZATopvr | Ruchi Guo,Shuhao Cao,Long Chen | ICLR 2023,Poster | A Transformer-based deep direct sampling method is proposed for electrical impedance tomography, a well-known severely ill-posed nonlinear boundary value inverse problem. A real-time reconstruction is achieved by evaluating the learned inverse operator between carefully designed data and the reconstructed images. An effort is made to give a specific example to a fundamental question: whether and how one can benefit from the theoretical structure of a mathematical problem to develop task-oriented and structure-conforming deep neural networks? Specifically, inspired by direct sampling methods for inverse problems, the 1D boundary data in different frequencies are preprocessed by a partial differential equation-based feature map to yield 2D harmonic extensions as different input channels. Then, by introducing learnable non-local kernels, the direct sampling is recast to a modified attention mechanism. The new method achieves superior accuracy over its predecessors and contemporary operator learners and shows robustness to noises in benchmarks.
This research shall strengthen the insights that, despite being invented for natural language processing tasks, the attention mechanism offers great flexibility to be modified in conformity with the a priori mathematical knowledge, which ultimately leads to the design of more physics-compatible neural architectures. | https://openreview.net/pdf/ba339ab66ec55a9c9e30f73d8db38087c06105e8.pdf |
Unicom: Universal and Compact Representation Learning for Image Retrieval | https://openreview.net/forum?id=3YFDsSRSxB- | https://openreview.net/forum?id=3YFDsSRSxB- | Xiang An,Jiankang Deng,Kaicheng Yang,Jaiwei Li,Ziyong Feng,Jia Guo,Jing Yang,Tongliang Liu | ICLR 2023,Poster | Modern image retrieval methods typically rely on fine-tuning pre-trained encoders to extract image-level descriptors.
However, the most widely used models are pre-trained on ImageNet-1K with limited classes. The pre-trained feature representation is therefore not universal enough to generalize well to the diverse open-world classes.
In this paper, we first cluster the large-scale \laion{} into one million pseudo classes based on the joint textual and visual features extracted by the CLIP model. Due to the confusion of label granularity, the automatically clustered dataset inevitably contains heavy inter-class conflict. To alleviate such conflict, we randomly select partial inter-class prototypes to construct the margin-based softmax loss. To further enhance the low-dimensional feature representation, we randomly select partial feature dimensions when calculating the similarities between embeddings and class-wise prototypes. The dual random partial selections are with respect to the class dimension and the feature dimension of the prototype matrix, making the classification conflict-robust and the feature embedding compact. Our method significantly outperforms state-of-the-art unsupervised and supervised image retrieval approaches on multiple benchmarks. The code and pre-trained models are released to facilitate future research \url{https://github.com/deepglint/unicom}. | https://openreview.net/pdf/e97855587823f27b93c38bd29a902d48e3a7c676.pdf |
Diffusion Probabilistic Fields | https://openreview.net/forum?id=ik91mY-2GN | https://openreview.net/forum?id=ik91mY-2GN | Peiye Zhuang,Samira Abnar,Jiatao Gu,Alex Schwing,Joshua M. Susskind,Miguel Ángel Bautista | ICLR 2023,Poster | Diffusion probabilistic models have quickly become a major approach for generative modeling of images, 3D geometry, video and other domains. However, to adapt diffusion generative modeling to these domains the denoising network needs to be carefully designed for each domain independently, oftentimes under the assumption that data lives in a Euclidean grid. In this paper we introduce Diffusion Probabilistic Fields (DPF), a diffusion model that can learn distributions over continuous functions defined over metric spaces, commonly known as fields. We extend the formulation of diffusion probabilistic models to deal with this field parametrization in an explicit way, enabling us to define an end-to-end learning algorithm that side-steps the requirement of representing fields with latent vectors as in previous approaches (Dupont et al., 2022a; Du et al., 2021). We empirically show that, while using the same denoising network, DPF effectively deals with different modalities like 2D images and 3D geometry, in addition to modeling distributions over fields defined on non-Euclidean metric spaces. | https://openreview.net/pdf/73ebf3d9d08b88d3b59f24b690fe3f2584b6d628.pdf |
Beyond calibration: estimating the grouping loss of modern neural networks | https://openreview.net/forum?id=6w1k-IixnL8 | https://openreview.net/forum?id=6w1k-IixnL8 | Alexandre Perez-Lebel,Marine Le Morvan,Gael Varoquaux | ICLR 2023,Poster | The ability to ensure that a classifier gives reliable confidence scores is essential to ensure informed decision-making. To this end, recent work has focused on miscalibration, i.e., the over or under confidence of model scores. Yet calibration is not enough: even a perfectly calibrated classifier with the best possible accuracy can have confidence scores that are far from the true posterior probabilities. This is due to the grouping loss, created by samples with the same confidence scores but different true posterior probabilities. Proper scoring rule theory shows that given the calibration loss, the missing piece to characterize individual errors is the grouping loss. While there are many estimators of the calibration loss, none exists for the grouping loss in standard settings. Here, we propose an estimator to approximate the grouping loss. We show that modern neural network architectures in vision and NLP exhibit grouping loss, notably in distribution shifts settings, which highlights the importance of pre-production validation. | https://openreview.net/pdf/4d8650f36612053fb6f48b9d246f2fa62c8c1a70.pdf |
Hybrid RL: Using both offline and online data can make RL efficient | https://openreview.net/forum?id=yyBis80iUuU | https://openreview.net/forum?id=yyBis80iUuU | Yuda Song,Yifei Zhou,Ayush Sekhari,Drew Bagnell,Akshay Krishnamurthy,Wen Sun | ICLR 2023,Poster | We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online interaction. The framework mitigates the challenges that arise in both pure offline and online RL settings, allowing for the design of simple and highly effective algorithms, in both theory and practice. We demonstrate these advantages by adapting the classical Q learning/iteration algorithm to the hybrid setting, which we call Hybrid Q-Learning or Hy-Q. In our theoretical results, we prove that the algorithm is both computationally and statistically efficient whenever the offline dataset supports a high-quality policy and the environment has bounded bilinear rank. Notably, we require no assumptions on the coverage provided by the initial distribution, in contrast with guarantees for policy gradient/iteration methods. In our experimental results, we show that Hy-Q with neural network function approximation outperforms state-of-the-art online, offline, and hybrid RL baselines on challenging benchmarks, including Montezuma’s Revenge. | https://openreview.net/pdf/dc7cd7793233a77b76033b4b3a179d9a40bc657c.pdf |
Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning | https://openreview.net/forum?id=p0yrSRbN5Bu | https://openreview.net/forum?id=p0yrSRbN5Bu | XIANGYU PENG,Chen Xing,Prafulla Kumar Choubey,Chien-Sheng Wu,Caiming Xiong | ICLR 2023,Poster | Prompt tuning approaches, which learn task-specific soft prompts for a downstream task conditioning on frozen pre-trained models, have attracted growing interest due to its parameter efficiency. With large language models and sufficient training data, prompt tuning performs comparably to full-model tuning. However, with limited training samples in few-shot settings, prompt tuning fails to match the performance of full-model fine-tuning. In this work, we focus on improving the few-shot performance of prompt tuning by transferring knowledge from soft prompts of source tasks with abundant training samples. Recognizing the good generalization capabilities of ensemble methods in low-data regime, we first experiment and show that a simple ensemble of model predictions based on different source prompts, outperforms existing multi-prompt knowledge transfer approaches such as source prompt fusion in the few-shot setting. Motivated by this observation, we further investigate model ensembles and propose Sample-specific Ensemble of Source Models (SESoM). SESoM learns to adjust the contribution of each source model for each target sample separately when ensembling source model outputs. Through this way, SESoM inherits the superior generalization of ensemble methods and simultaneously captures the sample-specific competence of each source prompt. We conduct experiments across a diverse set of eight NLP tasks using models of different scales (T5-\{base, large, XL\}) and find that SESoM consistently outperforms the existing models of the same as well as larger parametric scale by a large margin. | https://openreview.net/pdf/0da427b46bc6cf66b7284999d1ecd003cb8d4c3d.pdf |
GAIN: On the Generalization of Instructional Action Understanding | https://openreview.net/forum?id=RlPmWBiyp6w | https://openreview.net/forum?id=RlPmWBiyp6w | Junlong Li,Guangyi Chen,Yansong Tang,Jinan Bao,Kun Zhang,Jie Zhou,Jiwen Lu | ICLR 2023,Poster | Despite the great success achieved in instructional action understanding by deep learning and mountainous data, deploying trained models to the unseen environment still remains a great challenge, since it requires strong generalizability of models from in-distribution training data to out-of-distribution (OOD) data. In this paper, we introduce a benchmark, named GAIN, to analyze the GeneralizAbility of INstructional action understanding models. In GAIN, we reassemble steps of existing instructional video training datasets to construct the OOD tasks and then collect the corresponding videos. We evaluate the generalizability of models trained on in-distribution datasets with the performance on OOD videos and observe a significant performance drop. We further propose a simple yet effective approach, which cuts off the excessive contextual dependency of action steps by performing causal inference, to provide a potential direction for enhancing the OOD generalizability. In the experiments, we show that this simple approach can improve several baselines on both instructional action segmentation and detection tasks. We expect the introduction of the GAIN dataset will promote future in-depth research on the generalization of instructional video understanding. | https://openreview.net/pdf/d9991bcf24de9c97ed01f601f8d882714b2ee35e.pdf |
ManyDG: Many-domain Generalization for Healthcare Applications | https://openreview.net/forum?id=lcSfirnflpW | https://openreview.net/forum?id=lcSfirnflpW | Chaoqi Yang,M Brandon Westover,Jimeng Sun | ICLR 2023,Poster | The vast amount of health data has been continuously collected for each patient, providing opportunities to support diverse healthcare predictive tasks such as seizure detection and hospitalization prediction. Existing models are mostly trained on other patients’ data and evaluated on new patients. Many of them might suffer from poor generalizability. One key reason can be overfitting due to the unique information related to patient identities and their data collection environments, referred to as patient covariates in the paper. These patient covariates usually do not contribute to predicting the targets but are often difficult to remove. As a result, they can bias the model training process and impede generalization. In healthcare applications, most existing domain generalization methods assume a small number of domains. In this paper, considering the diversity of patient covariates, we propose a new setting by treating each patient as a separate domain (leading to many domains). We develop a new domain generalization method ManyDG, that can scale to such many-domain problems. Our method identifies the patient do- main covariates by mutual reconstruction, and removes them via an orthogonal projection step. Extensive experiments show that ManyDG can boost the generalization performance on multiple real-world healthcare tasks (e.g., 3.7% Jaccard improvements on MIMIC drug recommendation) and support realistic but challenging settings such as insufficient data and continuous learning. The code is available at https://github.com/ycq091044/ManyDG. | https://openreview.net/pdf/c38e48e8ca3abaebf91a325e0a5fff3a67fe7eb6.pdf |
DecAF: Joint Decoding of Answers and Logical Forms for Question Answering over Knowledge Bases | https://openreview.net/forum?id=XHc5zRPxqV9 | https://openreview.net/forum?id=XHc5zRPxqV9 | Donghan Yu,Sheng Zhang,Patrick Ng,Henghui Zhu,Alexander Hanbo Li,Jun Wang,Yiqun Hu,William Yang Wang,Zhiguo Wang,Bing Xiang | ICLR 2023,Poster | Question answering over knowledge bases (KBs) aims to answer natural language questions with factual information such as entities and relations in KBs. Previous methods either generate logical forms that can be executed over KBs to obtain final answers or predict answers directly. Empirical results show that the former often produces more accurate answers, but it suffers from non-execution issues due to potential syntactic and semantic errors in the generated logical forms. In this work, we propose a novel framework DecAF that jointly generates both logical forms and direct answers, and then combines the merits of them to get the final answers. Moreover, different from most of the previous methods, DecAF is based on simple free-text retrieval without relying on any entity linking tools --- this simplification eases its adaptation to different datasets. DecAF achieves new state-of-the-art accuracy on WebQSP, FreebaseQA, and GrailQA benchmarks, while getting competitive results on the ComplexWebQuestions benchmark. | https://openreview.net/pdf/68c87b5462ee10b7c77096c53bcc5edf61bc0d9e.pdf |
NANSY++: Unified Voice Synthesis with Neural Analysis and Synthesis | https://openreview.net/forum?id=elDEe8LYW7- | https://openreview.net/forum?id=elDEe8LYW7- | Hyeong-Seok Choi,Jinhyeok Yang,Juheon Lee,Hyeongju Kim | ICLR 2023,Poster | Various applications of voice synthesis have been developed independently despite the fact that they generate “voice” as output in common. In addition, most of the voice synthesis models still require a large number of audio data paired with annotated labels (e.g., text transcription and music score) for training. To this end, we propose a unified framework of synthesizing and manipulating voice signals from analysis features, dubbed NANSY++. The backbone network of NANSY++ is trained in a self-supervised manner that does not require any annotations paired with audio. After training the backbone network, we efficiently tackle four voice applications - i.e. voice conversion, text-to-speech, singing voice synthesis, and voice designing - by partially modeling the analysis features required for each task. Extensive experiments show that the proposed framework offers competitive advantages such as controllability, data efficiency, and fast training convergence, while providing high quality synthesis. Audio samples: tinyurl.com/8tnsy3uc. | https://openreview.net/pdf/bff332d5754f447736fe17ac241e9967a8ea220e.pdf |
Causality Compensated Attention for Contextual Biased Visual Recognition | https://openreview.net/forum?id=8XqDnrmZQNF | https://openreview.net/forum?id=8XqDnrmZQNF | Ruyang Liu,Jingjia Huang,Thomas H. Li,Ge Li | ICLR 2023,Poster | Visual attention does not always capture the essential object representation desired for robust predictions. Attention modules tend to underline not only the target object but also the common co-occurring context that the module thinks helpful in the training. The problem is rooted in the confounding effect of the context leading to incorrect causalities between objects and predictions, which is further exacerbated by visual attention. In this paper, to learn causal object features robust for contextual bias, we propose a novel attention module named Interventional Dual Attention (IDA) for visual recognition. Specifically, IDA adopts two attention layers with multiple sampling intervention, which compensates the attention against the confounder context. Note that our method is model-agnostic and thus can be implemented on various backbones. Extensive experiments show our model obtains significant improvements in classification and detection with lower computation. In particular, we achieve the state-of-the-art results in multi-label classification on MS-COCO and PASCAL-VOC. | https://openreview.net/pdf/bf453ea8d3f212490b7e43897f70ec14cc523533.pdf |
Multi-Objective Reinforcement Learning: Convexity, Stationarity and Pareto Optimality | https://openreview.net/forum?id=TjEzIsyEsQ6 | https://openreview.net/forum?id=TjEzIsyEsQ6 | Haoye Lu,Daniel Herman,Yaoliang Yu | ICLR 2023,Poster | In recent years, single-objective reinforcement learning (SORL) algorithms have received a significant amount of attention and seen some strong results. However, it is generally recognized that many practical problems have intrinsic multi-objective properties that cannot be easily handled by SORL algorithms. Although there have been many multi-objective reinforcement learning (MORL) algorithms proposed, there has been little recent exploration of the fundamental properties of the spaces we are learning in. In this paper, we perform a rigorous analysis of policy induced value functions and use the insights to distinguish three views of Pareto optimality. The results imply the convexity of the induced value function's range for stationary policies and suggest that any point of its Pareto front can be achieved by training a policy using linear scalarization (LS). We show the problem that leads to the suboptimal performance of LS can be solved by adding strongly concave terms to the immediate rewards, which motivates us to propose a new vector reward-based Q-learning algorithm, CAPQL. Combined with an actor-critic formulation, our algorithm achieves state-of-the-art performance on multiple MuJoCo tasks in the preference agnostic setting. Furthermore, we empirically show that, in contrast to other LS-based algorithms, our approach is significantly more stable, achieving similar results across various random seeds. | https://openreview.net/pdf/b1854b3d30ef316e856ad1f9b4e489239ef0e185.pdf |
Fooling SHAP with Stealthily Biased Sampling | https://openreview.net/forum?id=J4mJjotSauh | https://openreview.net/forum?id=J4mJjotSauh | gabriel laberge,Ulrich Aïvodji,Satoshi Hara,Mario Marchand,Foutse Khomh | ICLR 2023,Poster | SHAP explanations aim at identifying which features contribute the most to the difference in model prediction at a specific input versus
a background distribution. Recent studies have shown that they can be manipulated by malicious adversaries to produce arbitrary desired
explanations. However, existing attacks focus solely on altering the black-box model itself. In this paper, we propose a complementary family
of attacks that leave the model intact and manipulate SHAP explanations using stealthily biased sampling of the data points used to approximate expectations w.r.t the background distribution. In the context of fairness audit, we show that our attack can reduce the importance of a sensitive feature when explaining the difference in outcomes between groups while remaining undetected. More precisely, experiments performed on real-world datasets showed that our attack could yield up to a 90\% relative decrease in amplitude of the sensitive feature attribution. These results highlight the manipulability of SHAP explanations and encourage auditors to treat them with skepticism. | https://openreview.net/pdf/111674d48ee7b80bb7c9f66295467dfbe5ab65a1.pdf |
Asynchronous Gradient Play in Zero-Sum Multi-agent Games | https://openreview.net/forum?id=vPXp7K_Yhre | https://openreview.net/forum?id=vPXp7K_Yhre | Ruicheng Ao,Shicong Cen,Yuejie Chi | ICLR 2023,Poster | Finding equilibria via gradient play in competitive multi-agent games has been attracting a growing amount of attention in recent years, with emphasis on designing efficient strategies where the agents operate in a decentralized and symmetric manner with guaranteed convergence. While significant efforts have been made in understanding zero-sum two-player matrix games, the performance in zero-sum multi-agent games remains inadequately explored, especially in the presence of delayed feedbacks, leaving the scalability and resiliency of gradient play open to questions. In this paper, we make progress by studying asynchronous gradient plays in zero-sum polymatrix games under delayed feedbacks. We first establish that the last iterate of entropy-regularized optimistic multiplicative weight updates (OMWU) method converges linearly to the quantal response equilibrium (QRE), the solution concept under bounded rationality, in the absence of delays. The linear convergence continues to hold even when the feedbacks are randomly delayed under mild statistical assumptions, albeit at a slower rate. Moving beyond random delays, we further demonstrate entropy-regularized OMWU with two-timescale learning rates enjoys faster last-iterate convergence under fixed delays, and continues to converge provably even when the delays are arbitrarily bounded. Our methods also lead to finite-time guarantees to approximate the Nash equilibrium (NE) by moderating the amount of regularization. To the best of our knowledge, this work is the first that aims to understand asynchronous gradient play in zero-sum polymatrix games under a wide range of delay assumptions.
| https://openreview.net/pdf/e4a53ceb226d81ca0b14144280148b4353ae53d7.pdf |
Novel View Synthesis with Diffusion Models | https://openreview.net/forum?id=HtoA0oT30jC | https://openreview.net/forum?id=HtoA0oT30jC | Daniel Watson,William Chan,Ricardo Martin Brualla,Jonathan Ho,Andrea Tagliasacchi,Mohammad Norouzi | ICLR 2023,Poster | We present 3DiM (pronounced "three-dim"), a diffusion model for 3D novel view synthesis from as few as a single image. The core of 3DiM is an image-to-image diffusion model -- 3DiM takes a single reference view and their poses as inputs, and generates a novel view via diffusion. 3DiM can then generate a full 3D consistent scene following our novel stochastic conditioning sampler: the output frames of the scene are generated autoregressively, and during the reverse diffusion process of each individual frame, we select a random conditioning frame from the set of previous frames at each denoising step. We demonstrate that stochastic conditioning yields much more 3D consistent results compared to the naive sampling process which only conditions on a single previous frame. We compare 3DiMs to prior work on the SRN ShapeNet dataset, demonstrating that 3DiM's generated videos from a single view achieve much higher fidelity while being approximately 3D consistent. We also introduce a new evaluation methodology, 3D consistency scoring, to measure the 3D consistency of a generated object by training a neural field on the model's output views. 3DiMs are geometry free, do not rely on hyper-networks or test-time optimization for novel view synthesis, and allow a single model to easily scale to a large number of scenes. | https://openreview.net/pdf/fa3c83b8ced74fdc2103d3de7774170ced113e61.pdf |
DM-NeRF: 3D Scene Geometry Decomposition and Manipulation from 2D Images | https://openreview.net/forum?id=C_PRLz8bEJx | https://openreview.net/forum?id=C_PRLz8bEJx | Bing WANG,Lu Chen,Bo Yang | ICLR 2023,Poster | In this paper, we study the problem of 3D scene geometry decomposition and manipulation from 2D views. By leveraging the recent implicit neural representation techniques, particularly the appealing neural radiance fields, we introduce an object field component to learn unique codes for all individual objects in 3D space only from 2D supervision. The key to this component is a series of carefully designed loss functions to enable every 3D point, especially in non-occupied space, to be effectively optimized even without 3D labels. In addition, we introduce an inverse query algorithm to freely manipulate any specified 3D object shape in the learned scene representation. Notably, our manipulation algorithm can explicitly tackle key issues such as object collisions and visual occlusions. Our method, called DM-NeRF, is among the first to simultaneously reconstruct, decompose, manipulate and render complex 3D scenes in a single pipeline. Extensive experiments on three datasets clearly show that our method can accurately decompose all 3D objects from 2D views, allowing any interested object to be freely manipulated in 3D space such as translation, rotation, size adjustment, and deformation. | https://openreview.net/pdf/4f4eb3aacc4057088f93457c08b0973e42eebf1f.pdf |
Trading Information between Latents in Hierarchical Variational Autoencoders | https://openreview.net/forum?id=eWtMdr6yCmL | https://openreview.net/forum?id=eWtMdr6yCmL | Tim Z. Xiao,Robert Bamler | ICLR 2023,Poster | Variational Autoencoders (VAEs) were originally motivated as probabilistic generative models in which one performs approximate Bayesian inference. The proposal of $\beta$-VAEs breaks this interpretation and generalizes VAEs to application domains beyond generative modeling (e.g., representation learning, clustering, or lossy data compression) by introducing an objective function that allows practitioners to trade off between the information content ("bit rate") of the latent representation and the distortion of reconstructed data. In this paper, we reconsider this rate/distortion trade-off in the context of hierarchical VAEs, i.e., VAEs with more than one layer of latent variables. We propose a method to control each layer's contribution to the rate independently. We identify the most general class of inference models to which our proposed method is applicable, and we derive theoretical bounds on the performance of downstream tasks as functions of the individual layers' rates. Our experiments demonstrate that the proposed method allows us to better tune hierarchical VAEs for a diverse set of practical use cases. | https://openreview.net/pdf/2cd2bc0d7acddf526659f3d4815fd21f9c07f9f6.pdf |
ISAAC Newton: Input-based Approximate Curvature for Newton's Method | https://openreview.net/forum?id=0paCJSFW7j | https://openreview.net/forum?id=0paCJSFW7j | Felix Petersen,Tobias Sutter,Christian Borgelt,Dongsung Huh,Hilde Kuehne,Yuekai Sun,Oliver Deussen | ICLR 2023,Poster | We present ISAAC (Input-baSed ApproximAte Curvature), a novel method that conditions the gradient using selected second-order information and has an asymptotically vanishing computational overhead, assuming a batch size smaller than the number of neurons. We show that it is possible to compute a good conditioner based on only the input to a respective layer without a substantial computational overhead. The proposed method allows effective training even in small-batch stochastic regimes, which makes it competitive to first-order as well as second-order methods. | https://openreview.net/pdf/95ae0894a363214571625181917ded6cab3ff944.pdf |
Learning Human-Compatible Representations for Case-Based Decision Support | https://openreview.net/forum?id=r0xte-t40I | https://openreview.net/forum?id=r0xte-t40I | Han Liu,Yizhou Tian,Chacha Chen,Shi Feng,Yuxin Chen,Chenhao Tan | ICLR 2023,Poster | Algorithmic case-based decision support provides examples to help human make sense of predicted labels and aid human in decision-making tasks. Despite the promising performance of supervised learning, representations learned by supervised models may not align well with human intuitions: what models consider as similar examples can be perceived as distinct by humans. As a result, they have limited effectiveness in case-based decision support. In this work, we incorporate ideas from metric learning with supervised learning to examine the importance of alignment for effective decision support. In addition to instance-level labels, we use human-provided triplet judgments to learn human-compatible decision-focused representations. Using both synthetic data and human subject experiments in multiple classification tasks, we demonstrate that such representation is better aligned with human perception than representation solely optimized for classification. Human-compatible representations identify nearest neighbors that are perceived as more similar by humans and allow humans to make more accurate predictions, leading to substantial improvements in human decision accuracies (17.8% in butterfly vs. moth classification and 13.2% in pneumonia classification). | https://openreview.net/pdf/8a16439b8778f846cdadf78cfd6ce542a48cbfbe.pdf |
Long-Tailed Learning Requires Feature Learning | https://openreview.net/forum?id=S-h1oFv-mq | https://openreview.net/forum?id=S-h1oFv-mq | Thomas Laurent,James von Brecht,Xavier Bresson | ICLR 2023,Poster | We propose a simple data model inspired from natural data such as text or images, and use it to study the importance of learning features in order to achieve good generalization. Our data model follows a long-tailed distribution in the sense that some rare and uncommon subcategories have few representatives in the training set. In this context we provide evidence that a learner succeeds if and only if it identifies the correct features, and moreover derive non-asymptotic generalization error bounds that precisely quantify the penalty that one must pay for not learning features. | https://openreview.net/pdf/bd53d16e3aa9f810431b28a022d8123ea795ac69.pdf |
How to Exploit Hyperspherical Embeddings for Out-of-Distribution Detection? | https://openreview.net/forum?id=aEFaE0W5pAd | https://openreview.net/forum?id=aEFaE0W5pAd | Yifei Ming,Yiyou Sun,Ousmane Dia,Yixuan Li | ICLR 2023,Poster | Out-of-distribution (OOD) detection is a critical task for reliable machine learning. Recent advances in representation learning give rise to distance-based OOD detection, where testing samples are detected as OOD if they are relatively far away from the centroids or prototypes of in-distribution (ID) classes. However, prior methods directly take off-the-shelf contrastive losses that suffice for classifying ID samples, but are not optimally designed when test inputs contain OOD samples. In this work, we propose CIDER, a novel representation learning framework that exploits hyperspherical embeddings for OOD detection. CIDER jointly optimizes two losses to promote strong ID-OOD separability: a dispersion loss that promotes large angular distances among different class prototypes, and a compactness loss that encourages samples to be close to their class prototypes. We analyze and establish the unexplored relationship between OOD detection performance and the embedding properties in the hyperspherical space, and demonstrate the importance of dispersion and compactness. CIDER establishes superior performance, outperforming the latest rival by 13.33% in FPR95. Code is available at https://github.com/deeplearning-wisc/cider. | https://openreview.net/pdf/96af0ad9159e6cf51a92e3b3d04b6260cb65b51e.pdf |
AnyDA: Anytime Domain Adaptation | https://openreview.net/forum?id=yyLvxYBJV1B | https://openreview.net/forum?id=yyLvxYBJV1B | Omprakash Chakraborty,Aadarsh Sahoo,Rameswar Panda,Abir Das | ICLR 2023,Poster | Unsupervised domain adaptation is an open and challenging problem in computer vision. While existing research shows encouraging results in addressing cross-domain distribution shift on common benchmarks, they are often constrained to testing under a specific target setting, limiting their impact for many real-world applications. In this paper, we introduce a simple yet effective framework for anytime domain adaptation that is executable with dynamic resource constraints to achieve accuracy-efficiency trade-offs under domain-shifts. We achieve this by training a single shared network using both labeled source and unlabeled data, with switchable depth, width and input resolutions on the fly to enable testing under a wide range of computation budgets. Starting with a teacher network trained from a label-rich source domain, we utilize bootstrapped recursive knowledge distillation as a nexus between source and target domains to jointly train the student network with switchable subnetworks. Experiments on multiple datasets well demonstrate the superiority of our approach over state-of-the-art methods. | https://openreview.net/pdf/cd2f8530081a65f6a7fee97f47a107998f5a9a17.pdf |
Improving Deep Regression with Ordinal Entropy | https://openreview.net/forum?id=raU07GpP0P | https://openreview.net/forum?id=raU07GpP0P | Shihao Zhang,Linlin Yang,Michael Bi Mi,Xiaoxu Zheng,Angela Yao | ICLR 2023,Poster | In computer vision, it is often observed that formulating regression problems as a classification task yields better performance. We investigate this curious phenomenon and provide a derivation to show that classification, with the cross-entropy loss, outperforms regression with a mean squared error loss in its ability to learn high-entropy feature representations. Based on the analysis, we propose an ordinal entropy loss to encourage higher-entropy feature spaces while maintaining ordinal relationships to improve the performance of regression tasks. Experiments on synthetic and real-world regression tasks demonstrate the importance and benefits of increasing entropy for regression. | https://openreview.net/pdf/3fa28db76e89d4a3ce4e48a57a415e706ad74b51.pdf |
Unified Discrete Diffusion for Simultaneous Vision-Language Generation | https://openreview.net/forum?id=8JqINxA-2a | https://openreview.net/forum?id=8JqINxA-2a | Minghui Hu,Chuanxia Zheng,Zuopeng Yang,Tat-Jen Cham,Heliang Zheng,Chaoyue Wang,Dacheng Tao,Ponnuthurai N. Suganthan | ICLR 2023,Poster | The recently developed discrete diffusion model performs extraordinarily well in generation tasks, especially in the text-to-image task, showing great potential for modeling multimodal signals. In this paper, we leverage these properties and present a unified multimodal generation model, which can perform text-based, image-based, and even vision-language simultaneous generation using a single model. Specifically, we unify the discrete diffusion process for multimodal signals by proposing a unified Markov transition matrix and a unified objective. Moreover, we design a multimodal mutual attention module to highlight the inter-modal linkages, which is vital for multimodal generation. Extensive experiments indicate that our proposed method can perform comparably to the state-of-the-art solutions in various generation tasks. | https://openreview.net/pdf/d386e35cf9491b25a9017b063c051ef1d750c527.pdf |
Iterative Patch Selection for High-Resolution Image Recognition | https://openreview.net/forum?id=QCrw0u9LQ7 | https://openreview.net/forum?id=QCrw0u9LQ7 | Benjamin Bergner,Christoph Lippert,Aravindh Mahendran | ICLR 2023,Poster | High-resolution images are prevalent in various applications, such as autonomous driving and computer-aided diagnosis. However, training neural networks on such images is computationally challenging and easily leads to out-of-memory errors even on modern GPUs. We propose a simple method, Iterative Patch Selection (IPS), which decouples the memory usage from the input size and thus enables the processing of arbitrarily large images under tight hardware constraints. IPS achieves this by selecting only the most salient patches, which are then aggregated into a global representation for image recognition. For both patch selection and aggregation, a cross-attention based transformer is introduced, which exhibits a close connection to Multiple Instance Learning. Our method demonstrates strong performance and has wide applicability across different domains, training regimes and image sizes while using minimal accelerator memory. For example, we are able to finetune our model on whole-slide images consisting of up to 250k patches (>16 gigapixels) with only 5 GB of GPU VRAM at a batch size of 16. | https://openreview.net/pdf/a52c85bb29a8881da5948c29b7f308d4e586cb7d.pdf |
Fuzzy Alignments in Directed Acyclic Graph for Non-Autoregressive Machine Translation | https://openreview.net/forum?id=LSz-gQyd0zE | https://openreview.net/forum?id=LSz-gQyd0zE | Zhengrui Ma,Chenze Shao,Shangtong Gui,Min Zhang,Yang Feng | ICLR 2023,Poster | Non-autoregressive translation (NAT) reduces the decoding latency but suffers from performance degradation due to the multi-modality problem. Recently, the structure of directed acyclic graph has achieved great success in NAT, which tackles the multi-modality problem by introducing dependency between vertices. However, training it with negative log-likelihood loss implicitly requires a strict alignment between reference tokens and vertices, weakening its ability to handle multiple translation modalities. In this paper, we hold the view that all paths in the graph are fuzzily aligned with the reference sentence. We do not require the exact alignment but train the model to maximize a fuzzy alignment score between the graph and reference, which takes captured translations in all modalities into account. Extensive experiments on major WMT benchmarks show that our method substantially improves translation performance and increases prediction confidence, setting a new state of the art for NAT on the raw training data. | https://openreview.net/pdf/69c77c3cf5e152302524f0544984352da29c2d74.pdf |
Efficient Federated Domain Translation | https://openreview.net/forum?id=uhLAcrAZ9cJ | https://openreview.net/forum?id=uhLAcrAZ9cJ | Zeyu Zhou,Sheikh Shams Azam,Christopher Brinton,David I. Inouye | ICLR 2023,Poster | A central theme in federated learning (FL) is the fact that client data distributions are often not independent and identically distributed (IID), which has strong implications on the training process. While most existing FL algorithms focus on the conventional non-IID setting of class imbalance or missing classes across clients, in practice, the distribution differences could be more complex, e.g., changes in class conditional (domain) distributions. In this paper, we consider this complex case in FL wherein each client has access to only one domain distribution. For tasks such as domain generalization, most existing learning algorithms require access to data from multiple clients (i.e., from multiple domains) during training, which is prohibitive in FL. To address this challenge, we propose a federated domain translation method that generates pseudodata for each client which could be useful for multiple downstream learning tasks. We empirically demonstrate that our translation model is more resource-efficient (in terms of both communication and computation) and easier to train in an FL setting than standard domain translation methods. Furthermore, we demonstrate that the learned translation model enables use of state-of-the-art domain generalization methods in a federated setting, which enhances accuracy and robustness to increases in the synchronization period compared to existing methodology. | https://openreview.net/pdf/1c9f96c284bcd668a45cc1e02f89a0988b28eaeb.pdf |
3D Segmenter: 3D Transformer based Semantic Segmentation via 2D Panoramic Distillation | https://openreview.net/forum?id=4dZeBJ83oxk | https://openreview.net/forum?id=4dZeBJ83oxk | ZHENNAN WU,YANG LI,Yifei Huang,Lin Gu,Tatsuya Harada,Hiroyuki Sato | ICLR 2023,Poster | Recently, 2D semantic segmentation has witnessed a significant advancement thanks to the huge amount of 2D image datasets available. Therefore, in this work, we propose the first 2D-to-3D knowledge distillation strategy to enhance 3D semantic segmentation model with knowledge embedded in the latent space of powerful 2D models. Specifically, unlike standard knowledge distillation, where teacher and student models take the same data as input, we use 2D panoramas properly aligned with corresponding 3D rooms to train the teacher network and use the learned knowledge from 2D teacher to guide 3D student. To facilitate our research, we create a large-scale, fine-annotated 3D semantic segmentation benchmark, containing voxel-wise semantic labels and aligned panoramas of 5175 scenes. Based on this benchmark, we propose a 3D volumetric semantic segmentation network, which adapts Video Swin Transformer as backbone and introduces a skip connected linear decoder. Achieving a state-of-the-art performance, our 3D Segmenter is computationally efficient and only requires $3.8\%$ of the parameters compared to the prior art. Our code and data will be released upon acceptance. | https://openreview.net/pdf/bad84a91fee36327634c83f4ba69dc11b0e52751.pdf |
Clifford Neural Layers for PDE Modeling | https://openreview.net/forum?id=okwxL_c4x84 | https://openreview.net/forum?id=okwxL_c4x84 | Johannes Brandstetter,Rianne van den Berg,Max Welling,Jayesh K Gupta | ICLR 2023,Poster | Partial differential equations (PDEs) see widespread use in sciences and engineering to describe simulation of physical processes as scalar and vector fields interacting and coevolving over time. Due to the computationally expensive nature of their standard solution methods, neural PDE surrogates have become an active research topic to accelerate these simulations. However, current methods do not explicitly take into account the relationship between different fields and their internal components, which are often correlated. Viewing the time evolution of such correlated fields through the lens of multivector fields allows us to overcome these limitations. Multivector fields consist of scalar, vector, as well as higher-order components, such as bivectors and trivectors. Their algebraic properties, such as multiplication, addition and other arithmetic operations can be described by Clifford algebras. To our knowledge, this paper presents the first usage of such multivector representations together with Clifford convolutions and Clifford Fourier transforms in the context of deep learning. The resulting Clifford neural layers are universally applicable and will find direct use in the areas of fluid dynamics, weather forecasting, and the modeling of physical systems in general. We empirically evaluate the benefit of Clifford neural layers by replacing convolution and Fourier operations in common neural PDE surrogates by their Clifford counterparts on 2D Navier-Stokes and weather modeling tasks, as well as 3D Maxwell equations. For similar parameter count, Clifford neural layers consistently improve generalization capabilities of the tested neural PDE surrogates. | https://openreview.net/pdf/4878c2dc31b0c173eca7532de05380bef50dffb0.pdf |
GOOD: Exploring geometric cues for detecting objects in an open world | https://openreview.net/forum?id=W-nZDQyuy8D | https://openreview.net/forum?id=W-nZDQyuy8D | Haiwen Huang,Andreas Geiger,Dan Zhang | ICLR 2023,Poster | We address the task of open-world class-agnostic object detection, i.e., detecting every object in an image by learning from a limited number of base object classes. State-of-the-art RGB-based models suffer from overfitting the training classes and often fail at detecting novel-looking objects. This is because RGB-based models primarily rely on appearance similarity to detect novel objects and are also prone to overfitting short-cut cues such as textures and discriminative parts. To address these shortcomings of RGB-based object detectors, we propose incorporating geometric cues such as depth and normals, predicted by general-purpose monocular estimators. Specifically, we use the geometric cues to train an object proposal network for pseudo-labeling unannotated novel objects in the training set. Our resulting Geometry-guided Open-world Object Detector (GOOD) significantly improves detection recall for novel object categories and already performs well with only a few training classes. Using a single ``person'' class for training on the COCO dataset, GOOD surpasses SOTA methods by 5.0% AR@100, a relative improvement of 24%. The code has been made available at https://github.com/autonomousvision/good. | https://openreview.net/pdf/d55d4b336cddc4a1185f421d39dfd75b4af4e13e.pdf |
TabCaps: A Capsule Neural Network for Tabular Data Classification with BoW Routing | https://openreview.net/forum?id=OgbtSLESnI | https://openreview.net/forum?id=OgbtSLESnI | Jintai Chen,KuanLun Liao,Yanwen Fang,Danny Chen,Jian Wu | ICLR 2023,Poster | Records in a table are represented by a collection of heterogeneous scalar features. Previous work often made predictions for records in a paradigm that processed each feature as an operating unit, which requires to well cope with the heterogeneity. In this paper, we propose to encapsulate all feature values of a record into vectorial features and process them collectively rather than have to deal with individual ones, which directly captures the representations at the data level and benefits robust performances. Specifically, we adopt the concept of "capsules" to organize features into vectorial features, and devise a novel capsule neural network called "TabCaps" to process the vectorial features for classification. In TabCaps, a record is encoded into several vectorial features by some optimizable multivariate Gaussian kernels in the primary capsule layer, where each vectorial feature represents a specific "profile" of the input record and is transformed into senior capsule layer under the guidance of a new straightforward routing algorithm. The design of routing algorithm is motivated by the Bag-of-Words (BoW) model, which performs capsule feature grouping straightforwardly and efficiently, in lieu of the computationally complex clustering of previous routing algorithms. Comprehensive experiments show that TabCaps achieves competitive and robust performances in tabular data classification tasks. | https://openreview.net/pdf/d3467a186dcd15df473440f6c24fe4d2d8f569c0.pdf |
An Exact Poly-Time Membership-Queries Algorithm for Extracting a Three-Layer ReLU Network | https://openreview.net/forum?id=-CoNloheTs | https://openreview.net/forum?id=-CoNloheTs | Amit Daniely,Elad Granot | ICLR 2023,Poster | We consider the natural problem of learning a ReLU network from queries, which was recently remotivated by model extraction attacks. In this work, we present a polynomial-time algorithm that can learn a depth-two ReLU network from queries under mild general position assumptions. We also present a polynomial-time algorithm that, under mild general position assumptions, can learn a rich class of depth-three ReLU networks from queries. For instance, it can learn most networks where the number of first layer neurons is smaller than the dimension and the number of second layer neurons.
These two results substantially improve state-of-the-art: Until our work, polynomial-time algorithms were only shown to learn from queries depth-two networks under the assumption that either the underlying distribution is Gaussian (Chen et al. (2021)) or that the weights matrix rows are linearly independent (Milli et al. (2019)). For depth three or more, there were no known poly-time results. | https://openreview.net/pdf/9e485cce01d880d423fc6e5af2b45fb1883847e0.pdf |
Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning | https://openreview.net/forum?id=EXnIyMVTL8s | https://openreview.net/forum?id=EXnIyMVTL8s | Yujun Shi,Jian Liang,Wenqing Zhang,Vincent Tan,Song Bai | ICLR 2023,Poster | Federated learning aims to train models collaboratively across different clients without sharing data for privacy considerations. However, one major challenge for this learning paradigm is the data heterogeneity problem, which refers to the discrepancies between the local data distributions among various clients. To tackle this problem, we first study how data heterogeneity affects the representations of the globally aggregated models. Interestingly, we find that heterogeneous data results in the global model suffering from severe dimensional collapse, in which representations tend to reside in a lower-dimensional space instead of the ambient space. Moreover, we observe a similar phenomenon on models locally trained on each client and deduce that the dimensional collapse on the global model is inherited from local models. In addition, we theoretically analyze the gradient flow dynamics to shed light on how data heterogeneity result in dimensional collapse for local models. To remedy this problem caused by the data heterogeneity, we propose FedDecorr, a novel method that can effectively mitigate dimensional collapse in federated learning. Specifically, FedDecorr applies a regularization term during local training that encourages different dimensions of representations to be uncorrelated. FedDecorr, which is implementation-friendly and computationally-efficient, yields consistent improvements over baselines on standard benchmark datasets. Code: https://github.com/bytedance/FedDecorr. | https://openreview.net/pdf/604a58996edbc056a1d05e06f41f2d8809fce38b.pdf |
Evidential Uncertainty and Diversity Guided Active Learning for Scene Graph Generation | https://openreview.net/forum?id=xI1ZTtVOtlz | https://openreview.net/forum?id=xI1ZTtVOtlz | Shuzhou Sun,Shuaifeng Zhi,Janne Heikkilä,Li Liu | ICLR 2023,Poster | Scene Graph Generation (SGG) has already shown its great potential in various downstream tasks, but it comes at the price of a prohibitively expensive annotation process. To reduce the annotation cost, we propose using Active Learning (AL) for sampling the most informative data. However, directly porting current AL methods to the SGG task poses the following challenges: 1) unreliable uncertainty estimates, and 2) data bias problems. To deal with these challenges, we propose EDAL (\textbf{E}vidential Uncertainty and \textbf{D}iversity Guided Deep \textbf{A}ctive \textbf{L}earning), a novel AL framework tailored for the SGG task. For challenge 1), we start with Evidential Deep Learning (EDL) coupled with a global relationship mining approach to estimate uncertainty, which can effectively overcome the perturbations of open-set relationships and background-relationships to obtain reliable uncertainty estimates. To address challenge 2), we seek the diversity-based method and design the Context Blocking Module (CBM) and Image Blocking Module (IBM) to alleviate context-level bias and image-level bias, respectively. Experiments show that our AL framework can approach the performance of a fully supervised SGG model with only about $10\%$ annotation cost. Furthermore, our ablation studies indicate that introducing AL into the SGG will face many challenges not observed in other vision tasks that are successfully overcome by our new modules. | https://openreview.net/pdf/c37f8ea52ab1545e242ee87c5a41b74f2f63a0f1.pdf |
Anisotropic Message Passing: Graph Neural Networks with Directional and Long-Range Interactions | https://openreview.net/forum?id=socffUzSIlx | https://openreview.net/forum?id=socffUzSIlx | Moritz Thürlemann,Sereina Riniker | ICLR 2023,Poster | Graph neural networks have shown great potential for the description of a variety of chemical systems.
However, standard message passing does not explicitly account for long-range and directional interactions, for instance due to electrostatics.
In this work, an anisotropic state based on Cartesian multipoles is proposed as an addition to the existing hidden features.
With the anisotropic state, message passing can be modified to explicitly account for directional interactions.
Compared to existing models, this modification results in relatively little additional computational cost.
Most importantly, the proposed formalism offers as a distinct advantage the seamless integration of (1) anisotropic long-range interactions, (2) interactions with surrounding fields and particles that are not part of the graph, and (3) the fast multipole method.
As an exemplary use case, the application to quantum mechanics/molecular mechanics (QM/MM) systems is demonstrated. | https://openreview.net/pdf/053df17cd735f08020efdc0e0955d8d7852a9bf2.pdf |
SYNC: SAFETY-AWARE NEURAL CONTROL FOR STABILIZING STOCHASTIC DELAY-DIFFERENTIAL EQUATIONS | https://openreview.net/forum?id=_8mS2NE-HXN | https://openreview.net/forum?id=_8mS2NE-HXN | Jingdong Zhang,Qunxi Zhu,Wei Yang,Wei Lin | ICLR 2023,Poster | Stabilization of the systems described by \textit{stochastic delay}-differential equations (SDDEs) under preset conditions is a challenging task in the control community. Here, to achieve this task, we leverage neural networks to learn control policies using the information of the controlled systems in some prescribed regions. Specifically, two learned control policies, i.e., the neural deterministic controller (NDC) and the neural stochastic controller (NSC), work effectively in the learning procedures that rely on, respectively, the well-known LaSalle-type theorem and the newly-established theorem for guaranteeing the stochastic stability in SDDEs. We theoretically investigate the performance of the proposed controllers in terms of convergence time and energy cost. More practically and significantly, we improve our learned control policies through considering the situation where the controlled trajectories only evolve in some specific safety set. {\color{black} The practical validity of such control policies restricted in safety set is attributed to the theory that we further develop for safety and stability guarantees in SDDEs using the stochastic control barrier function and the spatial discretization}. We call this control as SYNC (\textbf{S}afet\textbf{Y}-aware \textbf{N}eural \textbf{C}ontrol). The efficacy of all the articulated control policies, including the SYNC, is demonstrated systematically by using representative control problems. | https://openreview.net/pdf/4002b61654096398083b7b7f6d86d31e6170b185.pdf |
Differentiable Mathematical Programming for Object-Centric Representation Learning | https://openreview.net/forum?id=1J-ZTr7aypY | https://openreview.net/forum?id=1J-ZTr7aypY | Adeel Pervez,Phillip Lippe,Efstratios Gavves | ICLR 2023,Poster | We propose topology-aware feature partitioning into $k$ disjoint partitions for given scene features as a method for object-centric representation learning. To this end, we propose to use minimum $s$-$t$ graph cuts as a partitioning method which is represented as a linear program. The method is topologically aware since it explicitly encodes neighborhood relationships in the image graph. To solve the graph cuts our solution relies on an efficient, scalable, and differentiable quadratic programming approximation. Optimizations specific to cut problems allow us to solve the quadratic programs and compute their gradients significantly more efficiently compared with the general quadratic programming approach. Our results show that our approach is scalable and outperforms existing methods on object discovery tasks with textured scenes and objects. | https://openreview.net/pdf/df6cd5a64bf37cec562c59143c37962f0245daab.pdf |
Scalable Subset Sampling with Neural Conditional Poisson Networks | https://openreview.net/forum?id=p8hMBcPtvju | https://openreview.net/forum?id=p8hMBcPtvju | Adeel Pervez,Phillip Lippe,Efstratios Gavves | ICLR 2023,Poster | A number of problems in learning can be formulated in terms of the basic primitive of sampling $k$ elements out of a universe of $n$ elements. This subset sampling operation cannot directly be included in differentiable models and approximations are essential. Current approaches take an \emph{order sampling} approach to sampling subsets and depend on differentiable approximations of the Top-$k$ operator for selecting the largest $k$ elements from a set. We present a simple alternative method for sampling subsets based on \emph{conditional Poisson sampling}. Unlike order sampling approaches, the parallel complexity of the proposed method is independent of the subset size which makes the method scalable to large subset sizes. We adapt the procedure to make it efficient and amenable to discrete gradient approximations for use in differentiable models. Furthermore, the method also allows the subset size parameter $k$ to be differentiable. We demonstrate our approach on model explanation, image sub-sampling and stochastic $k$-nearest neighbor tasks outperforming existing methods in accuracy, efficiency and scalability. | https://openreview.net/pdf/a513030b29e367c181e3cef2554754e8e2377c31.pdf |
Improved Convergence of Differential Private SGD with Gradient Clipping | https://openreview.net/forum?id=FRLswckPXQ5 | https://openreview.net/forum?id=FRLswckPXQ5 | Huang Fang,Xiaoyun Li,Chenglin Fan,Ping Li | ICLR 2023,Poster | Differential private stochastic gradient descent (DP-SGD) with gradient clipping (DP-SGD-GC) is an effective optimization algorithm that can train machine learning models with a privacy guarantee. Despite the popularity of DP-SGD-GC, its convergence in unbounded domain without the Lipschitz continuous assumption is less-understood; existing analysis of DP-SGD-GC either impose additional assumptions or end up with an utility bound that involves an non-vanishing bias term. In this work, for smooth and unconstrained problems, we improve the current analysis and show that DP-SGD-GC can achieve a vanishing utility bound without any bias term. Furthermore, when the noise generated from subsampled gradients is light-tailed, we prove that DP-SGD-GC can achieve nearly the same utility bound as DP-SGD applies to the Lipschitz continuous objectives. As a by-product, we propose a new clipping technique, called value clipping, to mitigate the computational overhead caused by the classic gradient clipping. Experiments on standard benchmark datasets are conducted to support our analysis. | https://openreview.net/pdf/c33017ab9b28f8e423d8877e14fc05727755427b.pdf |
Learning to Estimate Single-View Volumetric Flow Motions without 3D Supervision | https://openreview.net/forum?id=2vmGv5wPDBZ | https://openreview.net/forum?id=2vmGv5wPDBZ | Erik Franz,Barbara Solenthaler,Nils Thuerey | ICLR 2023,Poster | We address the challenging problem of jointly inferring the 3D flow and volumetric densities moving in a fluid from a monocular input video with a deep neural network. Despite the complexity of this task, we show that it is possible to train the corresponding networks without requiring any 3D ground truth for training. In the absence of ground truth data we can train our model with observations from real-world capture setups instead of relying on synthetic reconstructions. We make this unsupervised training approach possible by first generating an initial prototype volume which is then moved and transported over time without the need for volumetric supervision. Our approach relies purely on image-based losses, an adversarial discriminator network, and regularization. Our method can estimate long-term sequences in a stable manner, while achieving closely matching targets for inputs such as rising smoke plumes. | https://openreview.net/pdf/1e0270d228f98a4950bed8564bfe14cb4a730f9b.pdf |
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations | https://openreview.net/forum?id=n0Pb9T5kmb | https://openreview.net/forum?id=n0Pb9T5kmb | Xuyang Zhao,Tianqi Du,Yisen Wang,Jun Yao,Weiran Huang | ICLR 2023,Poster | Self-Supervised Learning (SSL) is a paradigm that leverages unlabeled data for model training. Empirical studies show that SSL can achieve promising performance in distribution shift scenarios, where the downstream and training distributions differ. However, the theoretical understanding of its transferability remains limited. In this paper, we develop a theoretical framework to analyze the transferability of self-supervised contrastive learning, by investigating the impact of data augmentation on it. Our results reveal that the downstream performance of contrastive learning depends largely on the choice of data augmentation. Moreover, we show that contrastive learning fails to learn domain-invariant features, which limits its transferability. Based on these theoretical insights, we propose a novel method called Augmentation-robust Contrastive Learning (ArCL), which guarantees to learn domain-invariant features and can be easily integrated with existing contrastive learning algorithms. We conduct experiments on several datasets and show that ArCL significantly improves the transferability of contrastive learning. | https://openreview.net/pdf/a1cdcd4e932e905af2e155c4da607a152a588623.pdf |
Temperature Schedules for self-supervised contrastive methods on long-tail data | https://openreview.net/forum?id=ejHUr4nfHhD | https://openreview.net/forum?id=ejHUr4nfHhD | Anna Kukleva,Moritz Böhle,Bernt Schiele,Hilde Kuehne,Christian Rupprecht | ICLR 2023,Poster | Most approaches for self-supervised learning (SSL) are optimised on curated balanced datasets, e.g. ImageNet, despite the fact that natural data usually exhibits long-tail distributions. In this paper, we analyse the behaviour of one of the most popular variants of SSL, i.e. contrastive methods, on imbalanced data. In particular, we investigate the role of the temperature parameter $\tau$ in the contrastive loss, by analysing the loss through the lens of average distance maximisation, and find that a large $\tau$ emphasises group-wise discrimination, whereas a small $\tau$ leads to a higher degree of instance discrimination. While $\tau$ has thus far been treated exclusively as a constant hyperparameter, in this work, we propose to employ a dynamic $\tau$ and show that a simple cosine schedule can yield significant improvements in the learnt representations. Such a schedule results in a constant `task switching' between an emphasis on instance discrimination and group-wise discrimination and thereby ensures that the model learns both group-wise features, as well as instance-specific details. Since frequent classes benefit from the former, while infrequent classes require the latter, we find this method to consistently improve separation between the classes in long-tail data without any additional computational cost. | https://openreview.net/pdf/46efcd340650eebe5c97e48b313eef76bd5609fc.pdf |
Deep Learning on Implicit Neural Representations of Shapes | https://openreview.net/forum?id=OoOIW-3uadi | https://openreview.net/forum?id=OoOIW-3uadi | Luca De Luigi,Adriano Cardace,Riccardo Spezialetti,Pierluigi Zama Ramirez,Samuele Salti,Luigi di Stefano | ICLR 2023,Poster | Implicit Neural Representations (INRs) have emerged in the last few years as a powerful tool to encode continuously a variety of different signals like images, videos, audio and 3D shapes. When applied to 3D shapes, INRs allow to overcome the fragmentation and shortcomings of the popular discrete representations used so far. Yet, considering that INRs consist in neural networks, it is not clear whether and how it may be possible to feed them into deep learning pipelines aimed at solving a downstream task. In this paper, we put forward this research problem and propose inr2vec, a framework that can compute a compact latent representation for an input INR in a single inference pass. We verify that inr2vec can embed effectively the 3D shapes represented by the input INRs and show how the produced embeddings can be fed into deep learning pipelines to solve several tasks by processing exclusively INRs. | https://openreview.net/pdf/0499ba490c5bce6235d155c9b5bbfcf6025a1d0f.pdf |
ImaginaryNet: Learning Object Detectors without Real Images and Annotations | https://openreview.net/forum?id=9MbhFHqrti9 | https://openreview.net/forum?id=9MbhFHqrti9 | Minheng Ni,Zitong Huang,Kailai Feng,Wangmeng Zuo | ICLR 2023,Poster | Without the demand of training in reality, humans are able of detecting a new category of object simply based on the language description on its visual characteristics. Empowering deep learning with this ability undoubtedly enables the neural network to handle complex vision tasks, e.g., object detection, without collecting and annotating real images. To this end, this paper introduces a novel challenging learning paradigm Imaginary-Supervised Object Detection (ISOD), where neither real images nor manual annotations are allowed for training object detectors. To resolve this challenge, we propose ImaginaryNet, a framework to synthesize images by combining pretrained language model and text-to-image synthesis model. Given a class label, the language model is used to generate a full description of a scene with a target object, and the text-to-image model is deployed to generate a photo-realistic image. With the synthesized images and class labels, weakly supervised object detection can then be leveraged to accomplish ISOD. By gradually introducing real images and manual annotations, ImaginaryNet can collaborate with other supervision settings to further boost detection performance. Experiments show that ImaginaryNet can (i) obtain about 75% performance in ISOD compared with the weakly supervised counterpart of the same backbone trained on real data, (ii) significantly improve the baseline while achieving state-of-the-art or comparable performance by incorporating ImaginaryNet with other supervision settings. Our code will be publicly available at https://github.com/kodenii/ImaginaryNet. | https://openreview.net/pdf/871b8a661c3de03ebac23389dedd92af1f741b4c.pdf |
Contextual bandits with concave rewards, and an application to fair ranking | https://openreview.net/forum?id=UT-_SVOyD1H | https://openreview.net/forum?id=UT-_SVOyD1H | Virginie Do,Elvis Dohmatob,Matteo Pirotta,Alessandro Lazaric,Nicolas Usunier | ICLR 2023,Poster | We consider Contextual Bandits with Concave Rewards (CBCR), a multi-objective bandit problem where the desired trade-off between the rewards is defined by a known concave objective function, and the reward vector depends on an observed stochastic context. We present the first algorithm with provably vanishing regret for CBCR without restrictions on the policy space, whereas prior works were restricted to finite policy spaces or tabular representations. Our solution is based on a geometric interpretation of CBCR algorithms as optimization algorithms over the convex set of expected rewards spanned by all stochastic policies. Building on Frank-Wolfe analyses in constrained convex optimization, we derive a novel reduction from the CBCR regret to the regret of a \emph{scalar-reward} bandit problem. We illustrate how to apply the reduction off-the-shelf to obtain algorithms for CBCR with both linear and general reward functions, in the case of non-combinatorial actions. Motivated by fairness in recommendation, we describe a special case of CBCR with rankings and fairness-aware objectives, leading to the first algorithm with regret guarantees for contextual combinatorial bandits with fairness of exposure. | https://openreview.net/pdf/0368dedadc4bf112469a574263d2c2fe4a7f427c.pdf |
Gradient Boosting Performs Gaussian Process Inference | https://openreview.net/forum?id=3VKiaagxw1S | https://openreview.net/forum?id=3VKiaagxw1S | Aleksei Ustimenko,Artem Beliakov,Liudmila Prokhorenkova | ICLR 2023,Poster | This paper shows that gradient boosting based on symmetric decision trees can be equivalently reformulated as a kernel method that converges to the solution of a certain Kernel Ridge Regression problem. Thus, we obtain the convergence to a Gaussian Process' posterior mean, which, in turn, allows us to easily transform gradient boosting into a sampler from the posterior to provide better knowledge uncertainty estimates through Monte-Carlo estimation of the posterior variance. We show that the proposed sampler allows for better knowledge uncertainty estimates leading to improved out-of-domain detection. | https://openreview.net/pdf/797255180363d64520e961b850394aba641bfde6.pdf |
Learning Zero-Shot Cooperation with Humans, Assuming Humans Are Biased | https://openreview.net/forum?id=TrwE8l9aJzs | https://openreview.net/forum?id=TrwE8l9aJzs | Chao Yu,Jiaxuan Gao,Weilin Liu,Botian Xu,Hao Tang,Jiaqi Yang,Yu Wang,Yi Wu | ICLR 2023,Poster | There is a recent trend of applying multi-agent reinforcement learning (MARL) to train an agent that can cooperate with humans in a zero-shot fashion without using any human data. The typical workflow is to first repeatedly run self-play (SP) to build a policy pool and then train the final adaptive policy against this pool. A crucial limitation of this framework is that every policy in the pool is optimized w.r.t. the environment reward function, which implicitly assumes that the testing partners of the adaptive policy will be precisely optimizing the same reward function as well. However, human objectives are often substantially biased according to their own preferences, which can differ greatly from the environment reward. We propose a more general framework, Hidden-Utility Self-Play (HSP), which explicitly models human biases as hidden reward functions in the self-play objective. By approximating the reward space as linear functions, HSP adopts an effective technique to generate an augmented policy pool with biased policies. We evaluate HSP on the Overcooked benchmark. Empirical results show that our HSP method produces higher rewards than baselines when cooperating with learned human models, manually scripted policies, and real humans. The HSP policy is also rated as the most assistive policy based on human feedback. | https://openreview.net/pdf/009d6d3a093049a11f22f96153117bbec6e44c65.pdf |
Modelling Long Range Dependencies in $N$D: From Task-Specific to a General Purpose CNN | https://openreview.net/forum?id=ZW5aK4yCRqU | https://openreview.net/forum?id=ZW5aK4yCRqU | David M Knigge,David W. Romero,Albert Gu,Efstratios Gavves,Erik J Bekkers,Jakub Mikolaj Tomczak,Mark Hoogendoorn,Jan-jakob Sonke | ICLR 2023,Poster | Performant Convolutional Neural Network (CNN) architectures must be tailored to specific tasks in order to consider the length, resolution, and dimensionality of the input data. In this work, we tackle the need for problem-specific CNN architectures. We present the Continuous Convolutional Neural Network (CCNN): a single CNN able to process data of arbitrary resolution, dimensionality and length without any structural changes. Its key component are its continuous convolutional kernels which model long-range dependencies at every layer, and thus remove the need of current CNN architectures for task-dependent downsampling and depths. We showcase the generality of our method by using the same architecture for tasks on sequential ($1{\rm D}$), visual ($2{\rm D}$) and point-cloud ($3{\rm D}$) data. Our CCNN matches and often outperforms the current state-of-the-art across all tasks considered. | https://openreview.net/pdf/abb196ec738da8172fc658b7f357a1883eea4e1e.pdf |
Planckian Jitter: countering the color-crippling effects of color jitter on self-supervised training | https://openreview.net/forum?id=Pia70sP2Oi1 | https://openreview.net/forum?id=Pia70sP2Oi1 | Simone Zini,Alex Gomez-Villa,Marco Buzzelli,Bartłomiej Twardowski,Andrew D. Bagdanov,Joost van de weijer | ICLR 2023,Poster | Several recent works on self-supervised learning are trained by mapping different augmentations of the same image to the same feature representation. The data augmentations used are of crucial importance to the quality of learned feature representations. In this paper, we analyze how the color jitter traditionally used in data augmentation negatively impacts the quality of the color features in learned feature representations. To address this problem, we propose a more realistic, physics-based color data augmentation - which we call Planckian Jitter - that creates realistic variations in chromaticity and produces a model robust to illumination changes that can be commonly observed in real life, while maintaining the ability to discriminate image content based on color information.
Experiments confirm that such a representation is complementary to the representations learned with the currently-used color jitter augmentation and that a simple concatenation leads to significant performance gains on a wide range of downstream datasets.
In addition, we present a color sensitivity analysis that documents the impact of different training methods on model neurons and shows that the performance of the learned features is robust with respect to illuminant variations.
Official code available at: https://github.com/TheZino/PlanckianJitter | https://openreview.net/pdf/14392e7c217114a74674c714c248cd648e9b92d3.pdf |
GAMR: A Guided Attention Model for (visual) Reasoning | https://openreview.net/forum?id=iLMgk2IGNyv | https://openreview.net/forum?id=iLMgk2IGNyv | Mohit Vaishnav,Thomas Serre | ICLR 2023,Poster | Humans continue to outperform modern AI systems in their ability to flexibly parse and understand complex visual scenes. Here, we present a novel module for visual reasoning, the Guided Attention Model for (visual) Reasoning ($\textit{GAMR}$), which instantiates an active vision theory -- positing that the brain solves complex visual reasoning problems dynamically -- via sequences of attention shifts to select and route task-relevant visual information into memory. Experiments on an array of visual reasoning tasks and datasets demonstrate GAMR's ability to learn visual routines in a robust and sample-efficient manner. In addition, GAMR is shown to be capable of zero-shot generalization on completely novel reasoning tasks. Overall, our work provides computational support for cognitive theories that postulate the need for a critical interplay between attention and memory to dynamically maintain and manipulate task-relevant visual information to solve complex visual reasoning tasks. | https://openreview.net/pdf/66fcdeb1236a4c6085e1902756d2129bbdca7f0e.pdf |
Voint Cloud: Multi-View Point Cloud Representation for 3D Understanding | https://openreview.net/forum?id=IpGgfpMucHj | https://openreview.net/forum?id=IpGgfpMucHj | Abdullah Hamdi,Silvio Giancola,Bernard Ghanem | ICLR 2023,Poster | Multi-view projection methods have demonstrated promising performance on 3D understanding tasks like 3D classification and segmentation. However, it remains unclear how to combine such multi-view methods with the widely available 3D point clouds. Previous methods use unlearned heuristics to combine features at the point level. To this end, we introduce the concept of the multi-view point cloud (Voint cloud), representing each 3D point as a set of features extracted from several view-points. This novel 3D Voint cloud representation combines the compactness of 3D point cloud representation with the natural view-awareness of multi-view representation. Naturally, we can equip this new representation with convolutional and pooling operations. We deploy a Voint neural network (VointNet) to learn representations in the Voint space. Our novel representation achieves state-of-the-art performance on 3D classification, shape retrieval, and robust 3D part segmentation on standard benchmarks ( ScanObjectNN, ShapeNet Core55, and ShapeNet Parts). Further analysis shows that VointNet improves the robustness to occlusion compared to other methods. | https://openreview.net/pdf/b3f85b26464b6cd916b9a66adb82d3d295c951c4.pdf |
Approximate Nearest Neighbor Search through Modern Error-Correcting Codes | https://openreview.net/forum?id=-jP_rDkyfpI | https://openreview.net/forum?id=-jP_rDkyfpI | Noam Touitou,Nissim Halabi | ICLR 2023,Poster | A locality-sensitive hash (or LSH) is a function that can efficiently map dataset points into a latent space while preserving pairwise distances. Such LSH functions have been used in approximate nearest-neighbor search (ANNS) in the following classic way, which we call classic hash clustering (CHC): first, the dataset points are hashed into a low-dimensional binary space using the LSH function; then, the points are clustered by these hash values. Upon receiving a query, its nearest neighbors are sought within its hash-cluster and nearby hash-clusters (i.e., multi-probe). However, CHC mandates a low-dimensional latent space for the LSH function, which distorts distances from the (high-dimensional) original real space; this results in inferior recall. This is often mitigated through using multiple hash tables at additional storage and memory costs.
In this paper, we introduce a better way of using LSH functions for ANNS. Our method, called the Polar Code Nearest-Neighbor (PCNN) algorithm, uses modern error-correcting codes (specifically polar codes) to maintain a manageable number of clusters inside a high-dimensional latent space. Allowing the LSH function to embed into this high-dimensional latent space results in higher recall, as the embedding faithfully captures distances in the original space. The crux of PCNN is using polar codes for probing: we present a multi-probe scheme for PCNN which uses efficient list-decoding methods for polar codes, with time complexity independent of the dataset size. Fixing the choice of LSH, experiment results demonstrate significant performance gains of PCNN over CHC; in particular, PCNN with a single table outperforms CHC with multiple tables, obviating the need for large memory and storage. | https://openreview.net/pdf/bb170de971ade1773293d4ad37a9265d38df34db.pdf |
When to Make and Break Commitments? | https://openreview.net/forum?id=q8vgHfPdoQP | https://openreview.net/forum?id=q8vgHfPdoQP | Alihan Hüyük,Zhaozhi Qian,Mihaela van der Schaar | ICLR 2023,Poster | In many scenarios, decision-makers must commit to long-term actions until their resolution before receiving the payoff of said actions, and usually, staying committed to such actions incurs continual costs. For instance, in healthcare, a newly-discovered treatment cannot be marketed to patients until a clinical trial is conducted, which both requires time and is also costly. Of course in such scenarios, not all commitments eventually pay off. For instance, a clinical trial might end up failing to show efficacy. Given the time pressure created by the continual cost of keeping a commitment, we aim to answer: When should a decision-maker break a commitment that is likely to fail—either to make an alternative commitment or to make no further commitments at all? First, we formulate this question as a new type of optimal stopping/switching problem called the optimal commitment problem (OCP). Then, we theoretically analyze OCP, and based on the insights we gain, propose a practical algorithm for solving it. Finally, we empirically evaluate the performance of our algorithm in running clinical trials with subpopulation selection. | https://openreview.net/pdf/93ca41d0b49f056e7cc0a55cc527e47244db6f7b.pdf |
DENSE RGB SLAM WITH NEURAL IMPLICIT MAPS | https://openreview.net/forum?id=QUK1ExlbbA | https://openreview.net/forum?id=QUK1ExlbbA | Heng Li,Xiaodong Gu,Weihao Yuan,luwei yang,Zilong Dong,Ping Tan | ICLR 2023,Poster | There is an emerging trend of using neural implicit functions for map representation in Simultaneous Localization and Mapping (SLAM). Some pioneer works have achieved encouraging results on RGB-D SLAM. In this paper, we present a dense RGB SLAM method with neural implicit map representation. To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder. This design effectively fuses shape cues across different scales to facilitate map reconstruction. Our method simultaneously solves the camera motion and the neural implicit map by matching the rendered and input video frames. To facilitate optimization, we further propose a photometric warping loss in the spirit of multi-view stereo to better constrain the camera pose and scene geometry. We evaluate our method on commonly used benchmarks and compare it with modern RGB and RGB-D SLAM systems. Our method achieves favorable results than previous methods and even surpasses some recent RGB-D SLAM methods.The code is at poptree.github.io/DIM-SLAM/. | https://openreview.net/pdf/ab0b52e87f06cf00151dc4f9780baeee6b12f597.pdf |
Monocular Scene Reconstruction with 3D SDF Transformers | https://openreview.net/forum?id=-iADdfa4GKH | https://openreview.net/forum?id=-iADdfa4GKH | Weihao Yuan,Xiaodong Gu,Heng Li,Zilong Dong,Siyu Zhu | ICLR 2023,Poster | Monocular scene reconstruction from posed images is challenging due to the complexity of a large environment. Recent volumetric methods learn to directly predict the TSDF volume and have demonstrated promising results in this task. However, most methods focus on how to extract and fuse the 2D features to a 3D feature volume, but none of them improve the way how the 3D volume is aggregated. In this work, we propose an SDF transformer network, which replaces the role of 3D CNN for better 3D feature aggregation. To reduce the explosive computation complexity of the 3D multi-head attention, we propose a sparse window attention module, where the attention is only calculated between the non-empty voxels within a local window. Then a top-down-bottom-up 3D attention network is built for 3D feature aggregation, where a dilate-attention structure is proposed to prevent geometry degeneration, and two global modules are employed to equip with global receptive fields. The experiments on multiple datasets show that this 3D transformer network generates a more accurate and complete reconstruction, which outperforms previous methods by a large margin. Remarkably, the mesh accuracy is improved by 41.8%, and the mesh completeness is improved by 25.3% on the ScanNet dataset. The code of our method will be made public. | https://openreview.net/pdf/3b48c9559475aa9f01470c60e369b6cc6d85672f.pdf |
Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network | https://openreview.net/forum?id=qU6NIcpaSi- | https://openreview.net/forum?id=qU6NIcpaSi- | Seungwoong Ha,Hawoong Jeong | ICLR 2023,Poster | Dynamical systems with interacting agents are universal in nature, commonly modeled by a graph of relationships between their constituents. Recently, various works have been presented to tackle the problem of inferring those relationships from the system trajectories via deep neural networks, but most of the studies assume binary or discrete types of interactions for simplicity. In the real world, the interaction kernels often involve continuous interaction strengths, which cannot be accurately approximated by discrete relations. In this work, we propose the relational attentive inference network (RAIN) to infer continuously weighted interaction graphs without any ground-truth interaction strengths. Our model employs a novel pairwise attention (PA) mechanism to refine the trajectory representations and a graph transformer to extract heterogeneous interaction weights for each pair of agents. We show that our RAIN model with the PA mechanism accurately infers continuous interaction strengths for simulated physical systems in an unsupervised manner. Further, RAIN with PA successfully predicts trajectories from motion capture data with an interpretable interaction graph, demonstrating the virtue of modeling unknown dynamics with continuous weights. | https://openreview.net/pdf/d2a42a569af4609051ef2c5ee6d8844663d49142.pdf |
From $t$-SNE to UMAP with contrastive learning | https://openreview.net/forum?id=B8a1FcY0vi | https://openreview.net/forum?id=B8a1FcY0vi | Sebastian Damrich,Niklas Böhm,Fred A Hamprecht,Dmitry Kobak | ICLR 2023,Poster | Neighbor embedding methods $t$-SNE and UMAP are the de facto standard for visualizing high-dimensional datasets. Motivated from entirely different viewpoints, their loss functions appear to be unrelated. In practice, they yield strongly differing embeddings and can suggest conflicting interpretations of the same data. The fundamental reasons for this and, more generally, the exact relationship between $t$-SNE and UMAP have remained unclear. In this work, we uncover their conceptual connection via a new insight into contrastive learning methods. Noise-contrastive estimation can be used to optimize $t$-SNE, while UMAP relies on negative sampling, another contrastive method. We find the precise relationship between these two contrastive methods, and provide a mathematical characterization of the distortion introduced by negative sampling. Visually, this distortion results in UMAP generating more compact embeddings with tighter clusters compared to $t$-SNE. We exploit this new conceptual connection to propose and implement a generalization of negative sampling, allowing us to interpolate between (and even extrapolate beyond) $t$-SNE and UMAP and their respective embeddings. Moving along this spectrum of embeddings leads to a trade-off between discrete / local and continuous / global structures, mitigating the risk of over-interpreting ostensible features of any single embedding. We provide a PyTorch implementation. | https://openreview.net/pdf/5fb70ac87069a4216afdf8eae1647285dbff46cc.pdf |
D4AM: A General Denoising Framework for Downstream Acoustic Models | https://openreview.net/forum?id=5fvXH49wk2 | https://openreview.net/forum?id=5fvXH49wk2 | Chi-Chang Lee,Yu Tsao,Hsin-Min Wang,Chu-Song Chen | ICLR 2023,Poster | The performance of acoustic models degrades notably in noisy environments. Speech enhancement (SE) can be used as a front-end strategy to aid automatic speech recognition (ASR) systems. However, existing training objectives of SE methods are not fully effective at integrating speech-text and noise-clean paired data for training toward unseen ASR systems. In this study, we propose a general denoising framework, D4AM, for various downstream acoustic models. Our framework fine-tunes the SE model with the backward gradient according to a specific acoustic model and the corresponding classification objective. In addition, our method aims to consider the regression objective as an auxiliary loss to make the SE model generalize to other unseen acoustic models. To jointly train an SE unit with regression and classification objectives, D4AM uses an adjustment scheme to directly estimate suitable weighting coefficients rather than undergoing a grid search process with additional training costs. The adjustment scheme consists of two parts: gradient calibration and regression objective weighting. The experimental results show that D4AM can consistently and effectively provide improvements to various unseen acoustic models and outperforms other combination setups. Specifically, when evaluated on the Google ASR API with real noisy data completely unseen during SE training, D4AM achieves a relative WER reduction of 24.65% compared with the direct feeding of noisy input. To our knowledge, this is the first work that deploys an effective combination scheme of regression (denoising) and classification (ASR) objectives to derive a general pre-processor applicable to various unseen ASR systems. Our code is available at https://github.com/ChangLee0903/D4AM. | https://openreview.net/pdf/520bdf66a9155c04855905babaead2b9aeba0b5d.pdf |
Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning | https://openreview.net/forum?id=lq62uWRJjiY | https://openreview.net/forum?id=lq62uWRJjiY | Qingru Zhang,Minshuo Chen,Alexander Bukharin,Pengcheng He,Yu Cheng,Weizhu Chen,Tuo Zhao | ICLR 2023,Poster | Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. However, common practice fine-tunes all of the parameters in a pre-trained model, which becomes prohibitive when a large number of downstream tasks are present. Therefore, many fine-tuning methods are proposed to learn incremental updates of pre-trained weights in a parameter efficient way, e.g., low-rank increments. These methods often evenly distribute the budget of incremental updates across all pre-trained weight matrices, and overlook the varying importance of different weight parameters. As a consequence, the fine-tuning performance is suboptimal. To bridge this gap, we propose AdaLoRA, which adaptively allocates the parameter budget among weight matrices according to their importance score. In particular, AdaLoRA parameterizes the incremental updates in the form of singular value decomposition. Such a novel approach allows us to effectively prune the singular values of unimportant updates, which is essentially to reduce their parameter budget but circumvent intensive exact SVD computations. We conduct extensive experiments with several pre-trained models on natural language processing, question answering, and natural language generation to validate the effectiveness of AdaLoRA. Results demonstrate that AdaLoRA manifests notable improvement over baselines, especially in the low budget settings. Our code is publicly available at https://github.com/QingruZhang/AdaLoRA . | https://openreview.net/pdf/00a236907b21d7118010fb705f96b4a934acff26.pdf |
Generalize Learned Heuristics to Solve Large-scale Vehicle Routing Problems in Real-time | https://openreview.net/forum?id=6ZajpxqTlQ | https://openreview.net/forum?id=6ZajpxqTlQ | Qingchun Hou,Jingwei Yang,Yiqiang Su,Xiaoqing Wang,Yuming Deng | ICLR 2023,Poster | Large-scale Vehicle Routing Problems (VRPs) are widely used in logistics, transportation, supply chain, and robotic systems. Recently, data-driven VRP heuristics are proposed to generate real-time VRP solutions with up to 100 nodes. Despite this progress, current heuristics for large-scale VRPs still face three major challenges: 1) Difficulty in generalizing the heuristics learned on small-scale VRPs to large-scale VRPs without retraining; 2) Challenge in generating real-time solutions for large-scale VRPs; 3) Difficulty in embedding global constraints into learned heuristics. We contribute in the three directions: We propose a Two-stage Divide Method (TAM) to generate sub-route sequence rather than node sequence for generalizing the heuristics learned on small-scale VRPs to solve large-scale VRPs in real-time. A two-step reinforcement learning method with new reward and padding techniques is proposed to train our TAM. A global mask function is proposed to keep the global constraints satisfied when dividing a large-scale VRP into several small-scale Traveling Salesman Problems (TSPs). As result, we can solve the small-scale TSPs in parallel quickly. The experiments on synthetic and real-world large-scale VRPs show our method could generalize the learned heuristics trained on datasets of VRP 100 to solve VRPs with over 5000 nodes in real-time while keeping the solution quality better than data-driven heuristics and competitive with traditional heuristics. | https://openreview.net/pdf/1360725553e9aebf2b149f234ac6d83c46a077d4.pdf |
Towards the Generalization of Contrastive Self-Supervised Learning | https://openreview.net/forum?id=XDJwuEYHhme | https://openreview.net/forum?id=XDJwuEYHhme | Weiran Huang,Mingyang Yi,Xuyang Zhao,Zihao Jiang | ICLR 2023,Poster | Recently, self-supervised learning has attracted great attention, since it only requires unlabeled data for model training. Contrastive learning is one popular method for self-supervised learning and has achieved promising empirical performance. However, the theoretical understanding of its generalization ability is still limited. To this end, we define a kind of $(\sigma,\delta)$-measure to mathematically quantify the data augmentation, and then provide an upper bound of the downstream classification error rate based on the measure. It reveals that the generalization ability of contrastive self-supervised learning is related to three key factors: alignment of positive samples, divergence of class centers, and concentration of augmented data. The first two factors are properties of learned representations, while the third one is determined by pre-defined data augmentation. We further investigate two canonical contrastive losses, InfoNCE and cross-correlation, to show how they provably achieve the first two factors. Moreover, we conduct experiments to study the third factor, and observe a strong correlation between downstream performance and the concentration of augmented data.
| https://openreview.net/pdf/1976cb32977735bb4ba6638db6e9254ec826ead0.pdf |
CO3: Cooperative Unsupervised 3D Representation Learning for Autonomous Driving | https://openreview.net/forum?id=QUaDoIdgo0 | https://openreview.net/forum?id=QUaDoIdgo0 | Runjian Chen,Yao Mu,Runsen Xu,Wenqi Shao,Chenhan Jiang,Hang Xu,Yu Qiao,Zhenguo Li,Ping Luo | ICLR 2023,Poster | Unsupervised contrastive learning for indoor-scene point clouds has achieved great successes. However, unsupervised representation learning on outdoor-scene point clouds remains challenging because previous methods need to reconstruct the whole scene and capture partial views for the contrastive objective. This is infeasible in outdoor scenes with moving objects, obstacles, and sensors. In this paper, we propose CO3, namely {Co}operative {Co}ntrastive Learning and {Co}ntextual Shape Prediction, to learn 3D representation for outdoor-scene point clouds in an unsupervised manner. CO3 has several merits compared to existing methods. (1) It utilizes LiDAR point clouds from vehicle-side and infrastructure-side to build views that differ enough but meanwhile maintain common semantic information for contrastive learning, which are more appropriate than views built by previous methods. (2) Alongside the contrastive objective, we propose contextual shape prediction to bring more task-relevant information for unsupervised 3D point cloud representation learning and we also provide a theoretical analysis for this pre-training goal. (3) As compared to previous methods, representation learned by CO3 is able to be transferred to different outdoor scene dataset collected by different type of LiDAR sensors. (4) CO3 improves current state-of-the-art methods on Once, KITTI and NuScenes datasets by up to 2.58 mAP in 3D object detection task and 3.54 mIoU in LiDAR semantic segmentation task. Codes and models will be released. | https://openreview.net/pdf/6e976109956ae2e167ab82471a37bc73ab696079.pdf |
Bag of Tricks for Unsupervised Text-to-Speech | https://openreview.net/forum?id=SbR9mpTuBn | https://openreview.net/forum?id=SbR9mpTuBn | Yi Ren,Chen Zhang,Shuicheng YAN | ICLR 2023,Poster | Unsupervised text-to-speech (TTS) aims to train TTS models for a specific language without any paired speech-text training data in that language. Existing methods either use speech and corresponding pseudo text generated by an unsupervised automatic speech recognition (ASR) model as training data, or employ the back-translation technique. Though effective, they suffer from low robustness to low-quality data and heavy dependence on the lexicon of a language that is sometimes unavailable, leading to difficulty in convergence, especially in low-resource language scenarios. In this work, we introduce a bag of tricks to enable effective unsupervised TTS. Specifically, 1) we carefully design a voice conversion model to normalize the variable and noisy information in the low-quality speech data while preserving the pronunciation information; 2) we employ the non-autoregressive TTS model to overcome the robustness issue; and 3) we explore several tricks applied in back-translation, including curriculum learning, length augmentation and auxiliary supervised loss to stabilize the back-translation and improve its effectiveness. Through experiments, it has been demonstrated that our method achieves better intelligibility and audio quality than all previous methods, and that these tricks are very essential to the performance gain. | https://openreview.net/pdf/f21bd890476de833a3d985565b4fb4d8efa1420c.pdf |
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy | https://openreview.net/forum?id=bZjxxYURKT | https://openreview.net/forum?id=bZjxxYURKT | Yan Sun,Li Shen,Tiansheng Huang,Liang Ding,Dacheng Tao | ICLR 2023,Poster | Federated learning (FL) is an emerging distributed machine learning framework which jointly trains a global model via a large number of local devices with data privacy protections. Its performance suffers from the non-vanishing biases introduced by the local inconsistent optimal and the rugged client-drifts by the local over-fitting. In this paper, we propose a novel and practical method, FedSpeed, to alleviate the negative impacts posed by these problems. Concretely, FedSpeed applies the prox-correction term on the current local updates to efficiently reduce the biases introduced by the prox-term, a necessary regularizer to maintain the strong local consistency. Furthermore, FedSpeed merges the vanilla stochastic gradient with a perturbation computed from an extra gradient ascent step in the neighborhood, thereby alleviating the issue of local over-fitting. Our theoretical analysis indicates that the convergence rate is related to both the communication rounds $T$ and local intervals $K$ with a tighter upper bound $\mathcal{O}(\frac{1}{T})$ if $K=\mathcal{O}(T)$. Moreover, we conduct extensive experiments on the real-world dataset to demonstrate the efficiency of our proposed FedSpeed, which converges significantly faster and achieves the state-of-the-art (SOTA) performance on the general FL experimental settings than several baselines including FedAvg, FedProx, FedCM, FedAdam, SCAFFOLD, FedDyn, FedADMM, etc. | https://openreview.net/pdf/a67b74b94c31bbb5dc3c6bbe829796b30ff25b6b.pdf |
Advancing Radiograph Representation Learning with Masked Record Modeling | https://openreview.net/forum?id=w-x7U26GM7j | https://openreview.net/forum?id=w-x7U26GM7j | Hong-Yu Zhou,Chenyu Lian,Liansheng Wang,Yizhou Yu | ICLR 2023,Poster | Modern studies in radiograph representation learning (R$^2$L) rely on either self-supervision to encode invariant semantics or associated radiology reports to incorporate medical expertise, while the complementarity between them is barely noticed. To explore this, we formulate the self- and report-completion as two complementary objectives and present a unified framework based on masked record modeling (MRM). In practice, MRM reconstructs masked image patches and masked report tokens following a multi-task scheme to learn knowledge-enhanced semantic representations. With MRM pre-training, we obtain pre-trained models that can be well transferred to various radiography tasks. Specifically, we find that MRM offers superior performance in label-efficient fine-tuning. For instance, MRM achieves 88.5% mean AUC on CheXpert using 1% labeled data, outperforming previous R$^2$L methods with 100% labels. On NIH ChestX-ray, MRM outperforms the best performing counterpart by about 3% under small labeling ratios. Besides, MRM surpasses self- and report-supervised pre-training in identifying the pneumonia type and the pneumothorax area, sometimes by large margins. | https://openreview.net/pdf/fc3034053cc948a50f2822650fee36b83e7cb54b.pdf |
Instance-wise Batch Label Restoration via Gradients in Federated Learning | https://openreview.net/forum?id=FIrQfNSOoTr | https://openreview.net/forum?id=FIrQfNSOoTr | Kailang Ma,Yu Sun,Jian Cui,Dawei Li,Zhenyu Guan,Jianwei Liu | ICLR 2023,Poster | Gradient inversion attacks have posed a serious threat to the privacy of federated learning. The attacks search for the optimal pair of input and label best matching the shared gradients and the search space of the attacks can be reduced by pre-restoring labels. Recently, label restoration technique allows for the extraction of labels from gradients analytically, but even the state-of-the-art remains limited to identify the presence of categories (i.e., the class-wise label restoration). This work considers the more real-world settings, where there are multiple instances of each class in a training batch. An analytic method is proposed to perform instance-wise batch label restoration from only the gradient of the final layer. On the basis of the approximate recovered class-wise embeddings and post-softmax probabilities, we establish linear equations of the gradients, probabilities and labels to derive the Number of Instances (NoI) per class by the Moore-Penrose pseudoinverse algorithm. Our experimental evaluations reach over 99% Label existence Accuracy (LeAcc) and exceed 96% Label number Accuracy (LnAcc) in most cases on three image datasets and four classification models. The two metrics are used to evaluate class-wise and instance-wise label restoration accuracy, respectively. And the recovery is made feasible even with a batch size of 4096 and partially negative activations (e.g., Leaky ReLU and Swish). Furthermore, we demonstrate that our method facilitates the existing gradient inversion attacks by exploiting the recovered labels, with an increase of 6-7 in PSNR on both MNIST and CIFAR100. Our code is
available at https://github.com/BUAA-CST/iLRG. | https://openreview.net/pdf/c944d53a6bbd6d2e400a4514d9e48ff0c7fb64a0.pdf |
Re-parameterizing Your Optimizers rather than Architectures | https://openreview.net/forum?id=B92TMCG_7rp | https://openreview.net/forum?id=B92TMCG_7rp | Xiaohan Ding,Honghao Chen,Xiangyu Zhang,Kaiqi Huang,Jungong Han,Guiguang Ding | ICLR 2023,Poster | The well-designed structures in neural networks reflect the prior knowledge incorporated into the models. However, though different models have various priors, we are used to training them with model-agnostic optimizers such as SGD. In this paper, we propose to incorporate model-specific prior knowledge into optimizers by modifying the gradients according to a set of model-specific hyper-parameters. Such a methodology is referred to as Gradient Re-parameterization, and the optimizers are named RepOptimizers. For the extreme simplicity of model structure, we focus on a VGG-style plain model and showcase that such a simple model trained with a RepOptimizer, which is referred to as RepOpt-VGG, performs on par with or better than the recent well-designed models. From a practical perspective, RepOpt-VGG is a favorable base model because of its simple structure, high inference speed and training efficiency. Compared to Structural Re-parameterization, which adds priors into models via constructing extra training-time structures, RepOptimizers require no extra forward/backward computations and solve the problem of quantization. We hope to spark further research beyond the realms of model structure design. Code and models https://github.com/DingXiaoH/RepOptimizers. | https://openreview.net/pdf/57ec913feceef224444c6d2c7c462da8697c8076.pdf |
Protein Representation Learning via Knowledge Enhanced Primary Structure Reasoning | https://openreview.net/forum?id=VbCMhg7MRmj | https://openreview.net/forum?id=VbCMhg7MRmj | Hong-Yu Zhou,Yunxiang Fu,Zhicheng Zhang,Bian Cheng,Yizhou Yu | ICLR 2023,Poster | Protein representation learning has primarily benefited from the remarkable development of language models (LMs). Accordingly, pre-trained protein models also suffer from a problem in LMs: a lack of factual knowledge. The recent solution models the relationships between protein and associated knowledge terms as the knowledge encoding objective. However, it fails to explore the relationships at a more granular level, i.e., the token level. To mitigate this, we propose Knowledge-exploited Auto-encoder for Protein (KeAP), which performs token-level knowledge graph exploration for protein representation learning. In practice, non-masked amino acids iteratively query the associated knowledge tokens to extract and integrate helpful information for restoring masked amino acids via attention. We show that KeAP can consistently outperform the previous counterpart on 9 representative downstream applications, sometimes surpassing it by large margins. These results suggest that KeAP provides an alternative yet effective way to perform knowledge enhanced protein representation learning. | https://openreview.net/pdf/e4ea10108dbde640764dcde0449ba8a267d56786.pdf |
The Provable Benefit of Unsupervised Data Sharing for Offline Reinforcement Learning | https://openreview.net/forum?id=MTTPLcwvqTt | https://openreview.net/forum?id=MTTPLcwvqTt | Hao Hu,Yiqin Yang,Qianchuan Zhao,Chongjie Zhang | ICLR 2023,Poster | Self-supervised methods have become crucial for advancing deep learning by leveraging data itself to reduce the need for expensive annotations. However, the question of how to conduct self-supervised offline reinforcement learning (RL) in a principled way remains unclear.
In this paper, we address this issue by investigating the theoretical benefits of utilizing reward-free data in linear Markov Decision Processes (MDPs) within a semi-supervised setting. Further, we propose a novel, Provable Data Sharing algorithm (PDS) to utilize such reward-free data for offline RL. PDS uses additional penalties on the reward function learned from labeled data to prevent overestimation, ensuring a conservative algorithm. Our results on various offline RL tasks demonstrate that PDS significantly improves the performance of offline RL algorithms with reward-free data. Overall, our work provides a promising approach to leveraging the benefits of unlabeled data in offline RL while maintaining theoretical guarantees. We believe our findings will contribute to developing more robust self-supervised RL methods.
| https://openreview.net/pdf/806e8342078d660ef637a7cf57bbd0d47d229211.pdf |
Modeling Sequential Sentence Relation to Improve Cross-lingual Dense Retrieval | https://openreview.net/forum?id=-bVsNeR56KS | https://openreview.net/forum?id=-bVsNeR56KS | Shunyu Zhang,Yaobo Liang,MING GONG,Daxin Jiang,Nan Duan | ICLR 2023,Poster | Recently multi-lingual pre-trained language models (PLM) such as mBERT and XLM-R have achieved impressive strides in cross-lingual dense retrieval. Despite its successes, they are general-purpose PLM while the multilingual PLM tailored for cross-lingual retrieval is still unexplored. Motivated by an observation that the sentences in parallel documents are approximately in the same order, which is universal across languages, we propose to model this sequential sentence relation to facilitate cross-lingual representation learning. Specifically, we propose a multilingual PLM called masked sentence model (MSM), which consists of a sentence encoder to generate the sentence representations, and a document encoder applied to a sequence of sentence vectors from a document. The document encoder is shared for all languages to model the universal sequential sentence relation across languages. To train the model, we propose a masked sentence prediction task, which masks and predicts the sentence vector via a hierarchical contrastive loss with sampled negatives. Comprehensive experiments on four cross-lingual retrieval tasks show MSM significantly outperforms existing advanced pre-training models, demonstrating the effectiveness and stronger cross-lingual retrieval capabilities of our approach. | https://openreview.net/pdf/8649b63e1d2dd8ed68f852a7da6ab541293d32a9.pdf |
DepthFL : Depthwise Federated Learning for Heterogeneous Clients | https://openreview.net/forum?id=pf8RIZTMU58 | https://openreview.net/forum?id=pf8RIZTMU58 | Minjae Kim,Sangyoon Yu,Suhyun Kim,Soo-Mook Moon | ICLR 2023,Poster | Federated learning is for training a global model without collecting private local data from clients. As they repeatedly need to upload locally-updated weights or gradients instead, clients require both computation and communication resources enough to participate in learning, but in reality their resources are heterogeneous. To enable resource-constrained clients to train smaller local models, width scaling techniques have been used, which reduces the channels of a global model. Unfortunately, width scaling suffers from heterogeneity of local models when averaging them, leading to a lower accuracy than when simply excluding resource-constrained clients from training. This paper proposes a new approach based on depth scaling called DepthFL. DepthFL defines local models of different depths by pruning the deepest layers off the global model, and allocates them to clients depending on their available resources. Since many clients do not have enough resources to train deep local models, this would make deep layers partially-trained with insufficient data, unlike shallow layers that are fully trained. DepthFL alleviates this problem by mutual self-distillation of knowledge among the classifiers of various depths within a local model. Our experiments show that depth-scaled local models build a global model better than width-scaled ones, and that self-distillation is highly effective in training data-insufficient deep layers. | https://openreview.net/pdf/d71d905418b534a4d81650cfdc74e174d78bea48.pdf |
Masked Image Modeling with Denoising Contrast | https://openreview.net/forum?id=1fZd4owfJP6 | https://openreview.net/forum?id=1fZd4owfJP6 | Kun Yi,Yixiao Ge,Xiaotong Li,Shusheng Yang,Dian Li,Jianping Wu,Ying Shan,Xiaohu Qie | ICLR 2023,Poster | Since the development of self-supervised visual representation learning from contrastive learning to masked image modeling (MIM), there is no significant difference in essence, that is, how to design proper pretext tasks for vision dictionary look-up. MIM recently dominates this line of research with state-of-the-art performance on vision Transformers (ViTs), where the core is to enhance the patch-level visual context capturing of the network via denoising auto-encoding mechanism. Rather than tailoring image tokenizers with extra training stages as in previous works, we unleash the great potential of contrastive learning on de- noising auto-encoding and introduce a pure MIM method, ConMIM, to produce simple intra-image inter-patch contrastive constraints as the sole learning objectives for masked patch prediction. We further strengthen the denoising mechanism with asymmetric designs, including image perturbations and model progress rates, to improve the network pre-training. ConMIM-pretrained models with various scales achieve competitive results on downstream image classification, semantic segmentation, object detection, and instance segmentation tasks, e.g., on ImageNet-1K classification, we achieve 83.9% top-1 accuracy with ViT-Small and 85.3% with ViT-Base without extra data for pre-training. Code will be available at https://github.com/TencentARC/ConMIM.
| https://openreview.net/pdf/0a992b36167d39752c459212325882e0c62c458e.pdf |
GoBigger: A Scalable Platform for Cooperative-Competitive Multi-Agent Interactive Simulation | https://openreview.net/forum?id=NnOZT_CR26Z | https://openreview.net/forum?id=NnOZT_CR26Z | Ming Zhang,Shenghan Zhang,Zhenjie Yang,Lekai Chen,Jinliang Zheng,Chao Yang,Chuming Li,Hang Zhou,Yazhe Niu,Yu Liu | ICLR 2023,Poster | The emergence of various multi-agent environments has motivated powerful algorithms to explore agents' cooperation or competition. Even though this has greatly promoted the development of multi-agent reinforcement learning (MARL), it is still not enough to support further exploration on the behavior of swarm intelligence between multiple teams, and cooperation between multiple agents due to their limited scalability. To alleviate this, we introduce GoBigger, a scalable platform for cooperative-competition multi-agent interactive simulation. GoBigger is an enhanced environment for the Agar-like game, enabling the simulation of multiple scales of agent intra-team cooperation and inter-team competition. Compared with existing multi-agent simulation environments, our platform supports multi-team games with more than two teams simultaneously, which dramatically expands the diversity of agent cooperation and competition, and can more effectively simulate the swarm intelligent agent behavior. Besides, in GoBigger, the cooperation between the agents in a team can lead to much higher performance. We offer a diverse set of challenging scenarios, built-in bots, and visualization tools for best practices in benchmarking. We evaluate several state-of-the-art algorithms on GoBigger and demonstrate the potential of the environment. We believe this platform can inspire various emerging research directions in MARL, swarm intelligence, and large-scale agent interactive learning. Both GoBigger and its related benchmark are open-sourced. More information could be found at https://github.com/opendilab/GoBigger. | https://openreview.net/pdf/a9b5c1cac35cce14dad1036f5b0f324ad899d11f.pdf |
Masked Unsupervised Self-training for Label-free Image Classification | https://openreview.net/forum?id=ZAKkiVxiAM9 | https://openreview.net/forum?id=ZAKkiVxiAM9 | Junnan Li,Silvio Savarese,Steven Hoi | ICLR 2023,Poster | State-of-the-art computer vision models are mostly trained with supervised learning using human-labeled images, which limits their scalability due to the expensive annotation cost. While self-supervised representation learning has achieved impressive progress, it still requires a second stage of finetuning on labeled data. On the other hand, models pre-trained with large-scale text supervision (e.g., CLIP) have enabled zero-shot transfer to downstream image classification tasks. However, the zero-shot performance of CLIP-like models are often insufficient for real-world adoption. In this paper, we aim to leverage the abundant unlabeled data from a target domain to improve the performance of a pre-trained zero-shot classifier, by unsupervised finetuning of the pre-trained model. We propose Masked Unsupervised Self-Training (MUST), a new approach which leverages two different and complimentary sources of training signals: pseudo-labels and raw images. MUST jointly optimizes three objectives to learn both class-level global feature and pixel-level local feature and enforces a regularization between the two. We demonstrate the efficacy of MUST on 8 downstream tasks across a variety of domains, where it improves upon CLIP by a large margin. MUST also outperforms supervised few-shot adaptation methods. It achieves a top-1 accuracy of 77.7% on ImageNet using ViT-B, +9.4% higher than CLIP, and +6.2% higher than 16-shot CLIP adaptation. Our code is available at https://github.com/salesforce/MUST. | https://openreview.net/pdf/4727dfde7523443e21ec3f97b19eeba777450c3e.pdf |
GeneFace: Generalized and High-Fidelity Audio-Driven 3D Talking Face Synthesis | https://openreview.net/forum?id=YfwMIDhPccD | https://openreview.net/forum?id=YfwMIDhPccD | Zhenhui Ye,Ziyue Jiang,Yi Ren,Jinglin Liu,Jinzheng He,Zhou Zhao | ICLR 2023,Poster | Generating photo-realistic video portraits with arbitrary speech audio is a crucial problem in film-making and virtual reality. Recently, several works explore the usage of neural radiance field (NeRF) in this task to improve 3D realness and image fidelity. However, the generalizability of previous NeRF-based methods is limited by the small scale of training data. In this work, we propose GeneFace, a generalized and high-fidelity NeRF-based talking face generation method, which can generate natural results corresponding to various out-of-domain audio. Specifically, we learn a variational motion generator on a large lip-reading corpus, and introduce a domain adaptative post-net to calibrate the result. Moreover, we learn a NeRF-based renderer conditioned on the predicted motion. A head-aware torso-NeRF is proposed to eliminate the head-torso separation problem. Extensive experiments show that our method achieves more generalized and high-fidelity talking face generation compared to previous methods. Video samples and source code are available at https://geneface.github.io . | https://openreview.net/pdf/ff5ac074b645e7832c6c2fa298a36dc32675d901.pdf |
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection | https://openreview.net/forum?id=3mRwyG5one | https://openreview.net/forum?id=3mRwyG5one | Hao Zhang,Feng Li,Shilong Liu,Lei Zhang,Hang Su,Jun Zhu,Lionel Ni,Heung-Yeung Shum | ICLR 2023,Poster | We present DINO (DETR with Improved deNoising anchOr boxes), a strong end-to-end object detector. DINO improves over previous DETR-like models in performance and efficiency by using a contrastive way for denoising training, a look forward twice scheme for box prediction, and a mixed query selection method for anchor initialization. DINO achieves 49.4AP in 12 epochs and 51.3AP in 24 epochs on COCO with a ResNet-50 backbone and multi-scale features, yielding a significant improvement of +6.0AP and +2.7AP, respectively, compared to DN-DETR, the previous best DETR-like model. DINO scales well in both model size and data size. Without bells and whistles, after pre-training on the Objects365 dataset with a SwinL backbone, DINO obtains the best results on both COCO val2017 (63.2AP) and test-dev (63.3AP) with model size under 1 billion parameters. Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results. The code will be available. | https://openreview.net/pdf/4043c25577a8b31c9252512dac52aaf15d845a55.pdf |
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