GenAI4ELab/papercli-papers huggingface dataset:
Few-Shot Transferable Robust Representation Learning via Bilevel Attacks
Authors: Hyeonjeong Ha; Minseon Kim; Sung Ju Hwang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Existing adversarial learning methods for enhancing the robustness of deep neural networks assume the availability of a large amount of data from which we can generate adversarial examples. However, in an adversarial meta-learning setting, the model needs to train with only a few adversarial examples to learn a robust model for unseen tasks, which is a very difficult goal to achieve. Further, learning transferable robust representations for unseen domains is a difficult problem even with a large amount of data. To tackle such a challenge, we propose a novel adversarial self-supervised meta-learning framework with bilevel attacks which aims to learn robust representations that can generalize across tasks and domains. Specifically, in the inner loop, we update the parameters of the given encoder by taking inner gradient steps using two different sets of augmented samples, and generate adversarial examples for each view by maximizing the instance classification loss. Then, in the outer loop, we meta-learn the encoder parameter to maximize the agreement between the two adversarial examples, which enables it to learn robust representations. We experimentally validate the effectiveness of our approach on unseen domain adaptation tasks, on which it achieves impressive performance. Specifically, our method significantly outperforms the state-of-the-art meta-adversarial learning methods on few-shot learning tasks, as well as self-supervised learning baselines in standard learning settings with large-scale datasets.
DexDeform: Dexterous Deformable Object Manipulation with Human Demonstrations and Differentiable Physics
Authors: Sizhe Li; Zhiao Huang; Tao Chen; Tao Du; Hao Su; Joshua B. Tenenbaum; Chuang Gan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In this work, we aim to learn dexterous manipulation of deformable objects using multi-fingered hands. Reinforcement learning approaches for dexterous rigid object manipulation would struggle in this setting due to the complexity of physics interaction with deformable objects. At the same time, previous trajectory optimization approaches with differentiable physics for deformable manipulation would suffer from local optima caused by the explosion of contact modes from hand-object interactions. To address these challenges, we propose DexDeform, a principled framework that abstracts dexterous manipulation skills from human demonstration, and refines the learned skills with differentiable physics. Concretely, we first collect a small set of human demonstrations using teleoperation. And we then train a skill model using demonstrations for planning over action abstractions in imagination. To explore the goal space, we further apply augmentations to the existing deformable shapes in demonstrations and use a gradient optimizer to refine the actions planned by the skill model. Finally, we adopt the refined trajectories as new demonstrations for finetuning the skill model. To evaluate the effectiveness of our approach, we introduce a suite of six challenging dexterous deformable object manipulation tasks. Compared with baselines, DexDeform is able to better explore and generalize across novel goals unseen in the initial human demonstrations. Additional materials can be found at our project website: https://sites.google.com/view/dexdeform.
Learning Disentanglement in Autoencoders through Euler Encoding
Authors: Jaehoon Cha; Jeyan Thiyagalingam
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Noting the importance of factorizing (or disentangling) the latent space, we propose a novel, non-probabilistic disentangling framework for autoencoders, based on the principles of symmetry transformations that are independent of one another. To the best of our knowledge, this is the first deterministic model that is aiming to achieve disentanglement based on autoencoders without pairs of images or labels, by explicitly introducing inductive biases into a model architecture through Euler encoding. The proposed model is then compared with a number of state-of-the-art models, relevant to disentanglement, including symmetry-based and generative models based on autoencoders. Our evaluation using six different disentanglement metrics, including the unsupervised disentanglement metric we propose here in this paper, shows that the proposed model can offer better disentanglement, especially when variances of the features are different, where other methods may struggle. We believe that this model opens several opportunities for linear disentangled representation learning based on deterministic autoencoders.
A Robustly and Effectively Optimized Pretraining Approach for Masked Autoencoder
Authors: Ruijia Xu; Yixiao Ge; Kun Yi; XUYUAN XU; Yexin Wang; Ying-Cong Chen; Hao Chen; Ying Shan
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Recently, Masked Image Modeling (MIM) has increasingly reshaped the status quo of self-supervised visual pre-training. This paper does not describe a novel MIM method, but to unravel several fundamental ingredients to robustly and effectively pre-train a Masked AutoEncoder (MAE) with improved downstream performance as a byproduct. We highlight the great significance for the whole autoencoder to encourage high-variance interactions across different tokens, while simultaneously for the reconstructed target to smooth the inter-patch variances. First, at the decoding phase, we apply the standard dropout upon the attention probabilities as noise to randomly mask out the edge connection across different tokens. Otherwise, their shortcut interactions might hinder the emergence of meaningful contextual representation. Second, we point out that the per-patch normalization will fail unless the patch pixels rely on some population statistics to reduce inter-patch variance and then smooth the reconstruction. Third, we show that autoencoders with different capacities encounter the issue to varying degrees and the learnable masked tokens can be employed to manipulate the variance dependent on its inserted position and ratio in the model. The proposed techniques here are simple and effective to benefit the pre-training of a masked autoencoder stably and obtain superior performance across different downstream tasks.
Distributional Meta-Gradient Reinforcement Learning
Authors: Haiyan Yin; Shuicheng YAN; Zhongwen Xu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Meta-gradient reinforcement learning (RL) algorithms have substantially boosted the performance of RL agents by learning an adaptive return. All the existing algorithms adhere to the same reward learning principle, where the adaptive return is simply formulated in the form of expected cumulative rewards, upon which the policy and critic update rules are specified under well-adopted distance metrics. In this paper, we present a novel algorithm that builds on the success of meta-gradient RL algorithms and effectively improves such algorithms by following a simple recipe, i.e., going beyond the expected return to formulate and learn the return in a more expressive form, value distributions. To this end, we first formulate a distributional return that could effectively capture bootstrapping and discounting behaviors over distributions, to form an informative distributional return target in value update. Then we derive an efficient meta update rule to learn the adaptive distributional return with meta-gradients. For empirical evaluation, we first present an illustrative example on a toy two-color grid-world domain, which validates the benefit of learning distributional return over expectation; then we conduct extensive comparisons on a large-scale RL benchmark Atari 2600, where we confirm that our proposed method with distributional return works seamlessly well with the actor-critic framework and leads to state-of-the-art median human normalized score among meta-gradient RL literature.
Nearing or Surpassing: Overall Evaluation of Human-Machine Dynamic Vision Ability
Authors: Shiyu Hu; Xin Zhao; Yipei Wang; Yanhu Shan; Kaiqi Huang
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Dynamic visual ability (DVA), a fundamental function of the human visual system, has been successfully modeled by many computer vision tasks in recent decades. However, the prosperity developments mainly concentrate on using deep neural networks (DNN) to simulate the human DVA system, but evaluation systems still simply compare performance between machines, making it tough to determine how far the gap is between humans and machines in dynamic vision tasks. In fact, neglecting this issue not only makes it hard to determine the correctness of current research routes, but also cannot truly measure the DVA intelligence of machines. To answer the question, this work designs a comprehensive evaluation system based on the 3E paradigm -- we carefully pick 87 videos from various dimensions to construct the environment, confirming it can cover both perceptual and cognitive components of DVA; select 20 representative machines and 15 human subjects to form the task executors, ensuring that different model structures can help us observe the effectiveness of research development; and finally quantify their DVA with a strict evaluation process. Based on detailed experimental analyses, we first determine that the current algorithm research route has effectively shortened the gap. Besides, we further summarize the weaknesses of different executors, and design a human-machine cooperation mechanism with superhuman performance. In summary, the contributions include: (1) Quantifying the DVA of humans and machines, (2) proposing a new view to evaluate DVA intelligence based on the human-machine comparison, and (3) providing a possibility of human-machine cooperation. The datasets, toolkits, codes, and evaluation metrics will be open-sourced to help researchers develop intelligent research on dynamic vision tasks.
Conditional Antibody Design as 3D Equivariant Graph Translation
Authors: Xiangzhe Kong; Wenbing Huang; Yang Liu
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Antibody design is valuable for therapeutic usage and biological research. Existing deep-learning-based methods encounter several key issues: 1) incomplete context for Complementarity-Determining Regions (CDRs) generation; 2) incapability of capturing the entire 3D geometry of the input structure; 3) inefficient prediction of the CDR sequences in an autoregressive manner. In this paper, we propose Multi-channel Equivariant Attention Network (MEAN) to co-design 1D sequences and 3D structures of CDRs. To be specific, MEAN formulates antibody design as a conditional graph translation problem by importing extra components including the target antigen and the light chain of the antibody. Then, MEAN resorts to E(3)-equivariant message passing along with a proposed attention mechanism to better capture the geometrical correlation between different components. Finally, it outputs both the 1D sequences and 3D structure via a multi-round progressive full-shot scheme, which enjoys more efficiency and precision against previous autoregressive approaches. Our method significantly surpasses state-of-the-art models in sequence and structure modeling, antigen-binding CDR design, and binding affinity optimization. Specifically, the relative improvement to baselines is about 23\% in antigen-binding CDR design and 34\% for affinity optimization.
Disentangling the Mechanisms Behind Implicit Regularization in SGD
Authors: Zachary Novack; Simran Kaur; Tanya Marwah; Saurabh Garg; Zachary Chase Lipton
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: A number of competing hypotheses have been proposed to explain why small-batch Stochastic Gradient Descent (SGD) leads to improved generalization over the full-batch regime, with recent work crediting the implicit regularization of various quantities throughout training. However, to date, empirical evidence assessing the explanatory power of these hypotheses is lacking. In this paper, we conduct an extensive empirical evaluation, focusing on the ability of various theorized mechanisms to close the small-to-large batch generalization gap. Additionally, we characterize how the quantities that SGD has been claimed to (implicitly) regularize change over the course of training. By using micro-batches, i.e. disjoint smaller subsets of each mini-batch, we empirically show that explicitly penalizing the gradient norm or the Fisher Information Matrix trace, averaged over micro-batches, in the large-batch regime recovers small-batch SGD generalization, whereas Jacobian-based regularizations fail to do so. This generalization performance is shown to often be correlated with how well the regularized model’s gradient norms resemble those of small-batch SGD. We additionally show that this behavior breaks down as the micro-batch size approaches the batch size. Finally, we note that in this line of inquiry, positive experimental findings on CIFAR10 are often reversed on other datasets like CIFAR100, highlighting the need to test hypotheses on a wider collection of datasets.
Resource Efficient Self-Supervised Learning for Speech Recognition
Authors: Abhinav Mehrotra; Alberto Gil Couto Pimentel Ramos; Nicholas Donald Lane; Sourav Bhattacharya
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Representation learning from sequential data using self-supervised learning (SSL) has proven to be a powerful technique and improved state-of-the-art (SOTA) results when fine tuned for various downstream tasks, including Automatic Speech Recognition (ASR). So far the success of SSL frameworks, e.g., Wav2Vec-2.0, for sequence-to-sequence (seq2seq) modeling is primarily carried out by masking intermediate features and then solving a contrastive task in an end-to-end manner. Although very successful, the overall training time (for example, days or weeks) and demanding resource requirements for achieving SOTA performance remain a significant barrier to further improving ASR solutions using such approaches. In this work we show that non-contrastive learning, such as an extension of the Barlow–Twins methodology, when applied to seq2seq SSL modeling improves convergence, while reducing training time. Our results show that Wav2Vec-2.0 architecture pre-training with a non-contrastive SSL approach reduces the GPU training hours by 2.3 times, compared to masking based SSL approaches, while achieving a significant improvement (i.e., up to 6% relative WER decrease) in the model performance for the ASR task. We further demonstrate that a combination of both masking based SSL and non-contrastive SSL improves the ASR performance, e.g., up to 12% relative WER decrease, for all splits of LibriSpeech evaluation dataset.
A new photoreceptor-inspired CNN layer enables deep learning models of retina to generalize across lighting conditions
Authors: Saad Idrees; Greg D Field; Frederick Rieke; Joel Zylberberg
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: As we move our eyes, and as lighting changes in our environment, the light intensity reaching our retinas changes dramatically and on multiple timescales. Despite these changing conditions, our retinas effortlessly extract visual information that allows downstream brain areas to make sense of the visual world. Such processing capabilities are desirable in many settings, including computer vision systems that operate in dynamic lighting environments like in self-driving cars, and in algorithms that translate visual inputs into neural signals for use in vision-restoring prosthetics. To mimic retinal processing, we first require models that can predict retinal ganglion cell (RGC) responses reliably. While existing state-of-the-art deep learning models can accurately predict RGC responses to visual scenes under steady-state lighting conditions, these models fail under dynamic lighting conditions. This is because changes in lighting markedly alter RGC responses: adaptation mechanisms dynamically tune RGC receptive fields on multiple timescales. Because current deep learning models of the retina have no in-built notion of light level or these adaptive mechanisms, they are unable to accurately predict RGC responses under lighting conditions that they were not trained on. We present here a new deep learning model of the retina that can predict RGC responses to visual scenes at different light levels without requiring training at each light level. Our model combines a fully trainable biophysical front end capturing the fast and slow adaptation mechanisms in the photoreceptors with convolutional neural networks (CNNs) capturing downstream retinal processing. We tested our model’s generalization performance across light levels using monkey and rat retinal data. Whereas conventional CNN models without the photoreceptor layer failed to predict RGC responses when the lighting conditions changed, our model with the photoreceptor layer as a front end fared much better in this challenge. Overall, our work demonstrates a new hybrid approach that equips deep learning models with biological vision mechanisms enabling them to adapt to dynamic environments.
CAKE: CAusal and collaborative proxy-tasKs lEarning for Semi-Supervised Domain Adaptation
Authors: Wenqiao Zhang; CHANGSHUO LIU; Can Cui; Beng Chin Ooi
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Semi-supervised domain adaptation (SSDA) adapts a learner to a new domain by effectively utilizing source domain data and a few labeled target samples. It is a practical yet under-investigated research topic. In this paper, we analyze the SSDA problem from two perspectives that have previously been overlooked, and correspondingly decompose it into two \emph{key subproblems}: \emph{robust domain adaptation (DA) learning} and \emph{maximal cross-domain data utilization}.  \textbf{(i)}  From a causal theoretical view, a robust DA model should distinguish the invariant ``concept'' (key clue to image label) from the nuisance of confounding factors across domains. To achieve this goal, we propose to generate \emph{concept-invariant samples} to enable the model to classify the samples through causal intervention, yielding improved generalization guarantees; \textbf{(ii)} Based on the robust DA theory, we aim to exploit the maximal utilization of rich source domain data and a few labeled target samples to boost SSDA further. Consequently, we propose a collaboratively debiasing learning framework that utilizes two complementary semi-supervised learning (SSL) classifiers to mutually exchange their unbiased knowledge, which helps unleash the potential of source and target domain training data, thereby producing more convincing pseudo-labels. Such obtained labels facilitate cross-domain feature alignment and duly improve the invariant concept learning. In our experimental study, we show that the proposed model significantly outperforms SOTA methods in terms of effectiveness and generalisability on SSDA datasets.
Transformers with Multiresolution Attention Heads
Authors: Tan Minh Nguyen; Tho Tran Huu; Tam Minh Nguyen; Minh Pham; Nhat Ho; Stanley Osher
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We propose the Transformer with Multiresolution-head Attention (MrsFormer), a class of efficient transformers inspired by the multiresolution approximation (MRA) for approximating a signal f using wavelet bases. MRA decomposes a signal into components that lie on orthogonal subspaces at different scales. Similarly, MrsFormer decomposes the attention heads in the multi-head attention into fine-scale and coarse-scale heads, modeling the attention patterns between tokens and between groups of tokens. Computing the attention heads in MrsFormer requires significantly less computation and memory footprint compared to the standard softmax transformer with multi-head attention. We analyze and validate the advantage of MrsFormer over the standard transformers on a wide range of applications including image and time series classification.
Deja Vu: Continual Model Generalization for Unseen Domains
Authors: Chenxi Liu; Lixu Wang; Lingjuan Lyu; Chen Sun; Xiao Wang; Qi Zhu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In real-world applications, deep learning models often run in non-stationary environments where the target data distribution continually shifts over time. There have been numerous domain adaptation (DA) methods in both online and offline modes to improve cross-domain adaptation ability. However, these DA methods typically only provide good performance after a long period of adaptation, and perform poorly on new domains before and during adaptation – in what we call the “Unfamiliar Period”, especially when domain shifts happen suddenly and significantly. On the other hand, domain generalization (DG) methods have been proposed to improve the model generalization ability on unadapted domains. However, existing DG works are ineffective for continually changing domains due to severe catastrophic forgetting of learned knowledge. To overcome these limitations of DA and DG in handling the Unfamiliar Period during continual domain shift, we propose RaTP, a framework that focuses on improving models’ target domain generalization (TDG) capability, while also achieving effective target domain adaptation (TDA) capability right after training on certain domains and forgetting alleviation (FA) capability on past domains. RaTP includes a training-free data augmentation module to prepare data for TDG, a novel pseudo-labeling mechanism to provide reliable supervision for TDA, and a prototype contrastive alignment algorithm to align different domains for achieving TDG, TDA and FA. Extensive experiments on Digits, PACS, and DomainNet demonstrate that RaTP significantly outperforms state-of-the-art works from Continual DA, Source-Free DA, Test-Time/Online DA, Single DG, Multiple DG and Unified DA&DG in TDG, and achieves comparable TDA and FA capabilities.
Enriching Online Knowledge Distillation with Specialist Ensemble
Authors: Mincheol Park; Woojeong Kim; Junsik Bang; Won Woo Ro; Suhyun Kim
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Online Knowledge Distillation (KD) has an advantage over traditional KD works in that it removes the necessity for a pre-trained teacher. Indeed, an ensemble of small teachers has become typical guidance for a student's learning trajectory. Previous works emphasized diversity to create helpful ensemble knowledge and further argued that the size of diversity should be significant to prevent homogenization. This paper proposes a well-founded online KD framework with naturally derived specialists. In supervised learning, the parameters of a classifier are optimized by stochastic gradient descent based on a training dataset distribution. If the training dataset is shifted, the optimal point and corresponding parameters change accordingly, which is natural and explicit.
We first introduce a label prior shift to induce evident diversity among the same teachers, which assigns a skewed label distribution to each teacher and simultaneously specializes them through importance sampling. Compared to previous works, our specialization achieves the highest level of diversity and maintains it throughout training. Second, we propose a new aggregation that uses post-compensation in specialist outputs and conventional model averaging. The aggregation empirically exhibits the advantage of ensemble calibration even if applied to previous diversity-eliciting methods. Finally, through extensive experiments, we demonstrate the efficacy of our framework on top-1 error rate, negative log-likelihood, and notably expected calibration error.