pwc-archive/papers-with-abstracts huggingface dataset:
Latent Feature Disentanglement For Visual Domain Generalization
Authors: Behnam Gholami, Mostafa El-Khamy, Kee-Bong Song
Date: 2021-09-29 00:00:00
Tasks: Data Augmentation, Disentanglement, Domain Generalization, Image-to-Image Translation

Despite remarkable success in a variety of computer vision applications, it is well-known that deep learning can fail catastrophically when presented with out-of-distribution data, where there are usually style differences between the training and test images. Toward addressing this challenge, we consider the domain generalization problem, wherein predictors are trained using data drawn from a family of related training (source) domains and then evaluated on a distinct and unseen test domain. Naively training a model on the aggregate set of data (pooled from all source domains) has been shown to perform suboptimally, since the information learned by that model might be domain-specific and generalize imperfectly to test domains. Data augmentation has been shown to be an effective approach to overcome this problem. However, its application has been limited to enforcing invariance to simple transformations like rotation, brightness change, etc. Such perturbations do not necessarily cover plausible real-world variations that preserve the semantics of the input (such as a change in the image style). In this paper, taking the advantage of multiple source domains, we propose a novel approach to express and formalize robustness to these kinds of real-world perturbations of the images. The three key ideas underlying our formulation are (1) leveraging disentangled representations of the images to define different factors of variations, (2) generating perturbed images by changing such factors composing the representations of the images. (3) enforcing the learner (classifier) to be invariant to such change in the images. We use image to image translation models to demonstrate the efficacy of this framework. Based on this, we propose a domain-invariant regularization (DIR) loss function, that enforces invariant prediction of targets (class labels) across domains which yields improved generalization performance. We demonstrate the effectiveness of our approach on several widely used datasets for the domain generalization problem, on all of which we achieve competitive results with state-of-the-art models.
Neural Energy Minimization for Molecular Conformation Optimization
Authors: Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Computational chemistry

This paper studies an important problem in computational chemistry: predicting a molecule's spatial atom arrangements, or a molecular conformation. We propose a neural energy minimization formulation that casts the prediction problem into an unrolled optimization process, where a neural network is parametrized to learn the gradient fields of a conformational energy landscape. Assuming different forms of the underlying potential energy function, we can not only reinterpret and unify many of the existing models but also derive new variants of SE(3)-equivariant neural networks in a principled manner. In our experiments, these new variants show superior performance in molecular conformation optimization comparing to existing SE(3)-equivariant neural networks. Moreover, our energy minimization formulation is also suitable for molecular conformation generation, where we can generate more diverse and accurate conformers comparing to existing baselines.
Distilling GANs with Style-Mixed Triplets for X2I Translation with Limited Data
Authors: Yaxing Wang, Joost Van de Weijer, Lu Yu, Shangling Jui
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Image Generation, Knowledge Distillation, Semantic Segmentation, Translation

Conditional image synthesis is an integral part of many X2I translation systems, including image-to-image, text-to-image and audio-to-image translation systems. Training these large systems generally requires huge amounts of training data. 
Therefore, we investigate knowledge distillation to transfer knowledge from a high-quality unconditioned generative model (e.g., StyleGAN) to a conditioned synthetic image generation modules in a variety of systems. To initialize the conditional and reference branch (from a unconditional GAN)  we exploit the style mixing characteristics of high-quality GANs to generate an infinite supply of style-mixed triplets to perform the knowledge distillation. Extensive experimental results in a number of image generation tasks (i.e., image-to-image, semantic segmentation-to-image, text-to-image and audio-to-image) demonstrate qualitatively and quantitatively that our method successfully transfers knowledge to the synthetic image generation modules, resulting in more realistic images than previous methods as confirmed by a significant drop in the FID.
Demystifying Hyperparameter Optimization in Federated Learning
Authors: Syed Zawad, Jun Yi, Minjia Zhang, Cheng Li, Feng Yan, Yuxiong He
Date: 2021-09-29 00:00:00
Tasks: Federated Learning, Hyperparameter Optimization, Privacy Preserving

Federated Learning (FL) is a new machine learning paradigm that enables training models collaboratively across clients without sharing private data. In FL, data is non-uniformly distributed among clients (i.e.,  data heterogeneity) and cannot be balanced nor monitored like in conventional ML. Such data heterogeneity and privacy requirements bring unique challenges for learning hyperparameter optimization as the training dynamics change across clients even within the same training round and they are difficult to measure due to privacy constraints. State-of-the-art frameworks in FL focus on developing better aggregation algorithms and policies with the aim of mitigating these challenges. However, almost all existing FL systems adopt a ``global'' tuning method that uses a single set of learning hyperparameters across all the clients, regardless of their underlying data distributions. Our study shows that such a widely adopted global tuning method is not suitable for FL due to its data heterogeneity-oblivious nature. We demonstrate that the data quantity and distribution of the clients have a significant impact on the choice of hyperparameters, making it necessary to have customized tuning for each client. Based on these observations, we propose a first of its kind heterogeneity-aware hyperparameter optimization methodology, FedTune, that adopts a proxy data based hyperparameter customization approach to address the privacy and tuning cost challenges. Together with a Bayesian strengthened tuner, the proposed customized tuning approach is effective, lightweight, and privacy preserving. Extensive evaluation demonstrates that FedTune can achieve up to 7/4/4/6% better accuracy than the widely adopted globally tuned method for popular FL benchmarks FEMNIST, Cifar100, Cifar10, and Fashion-MNIST respectively.
SGD Can Converge to Local Maxima
Authors: Liu Ziyin, Botao Li, James B Simon, Masahito Ueda
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

Stochastic gradient descent (SGD) is widely used for the nonlinear, nonconvex problem of training deep neural networks, but its behavior remains poorly understood. Many theoretical works have studied SGD, but they commonly rely on restrictive and unrealistic assumptions about the nature of its noise. In this work, we construct example optimization problems illustrating that, if these assumptions are relaxed, SGD can exhibit many strange behaviors that run counter to the established wisdom of the field. Our constructions show that (1) SGD can converge to local maxima, (2) SGD might only escape saddle points arbitrarily slowly, (3) SGD can prefer sharp minima over flat ones, and (4) AMSGrad can converge to local maxima. We realize our most surprising results in a simple neural network-like construction, suggesting their relevance to deep learning.
Towards Understanding the Robustness Against Evasion Attack on Categorical Data
Authors: Hongyan Bao, Yufei Han, Yujun Zhou, Yun Shen, Xiangliang Zhang
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Classification

Characterizing and assessing the adversarial vulnerability of classification models with categorical input has been a practically important, while rarely explored research problem. Our work echoes the challenge by first unveiling the impact factors of adversarial vulnerability of classification models with categorical data based on an information-theoretic adversarial risk analysis about the targeted classifier. Though certifying the robustness of such classification models is intrinsically an NP-hard combinatorial problem, our study shows that the robustness certification can be solved via an efficient greedy exploration of the discrete attack space for any measurable classifiers with a mild smoothness constraint. Our proposed robustness certification framework is instantiated with deep neural network models applied on real-world safety-critic data sources. Our empirical observations confirm the impact of the key adversarial risk factors with categorical input.
Bayesian Exploration for Lifelong Reinforcement Learning
Authors: Haotian Fu, Shangqun Yu, Michael Littman, George Konidaris
Date: 2021-09-29 00:00:00
Tasks: Lifelong learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A central question in reinforcement learning (RL) is how to leverage prior knowledge to accelerate learning in new tasks.  We propose a Bayesian exploration method for lifelong reinforcement learning (BLRL) that aims to learn a Bayesian posterior that distills the common structure shared across different tasks. We further derive a sample complexity analysis of BLRL in the finite MDP setting. To scale our approach, we propose a variational Bayesian Lifelong Learning (VBLRL) algorithm that is based on Bayesian neural networks, can be combined with recent model-based RL methods, and exhibits backward transfer. Experimental results on three challenging domains show that our algorithms adapt to new tasks faster than state-of-the-art lifelong RL methods.
Fight fire with fire: countering bad shortcuts in imitation learning with good shortcuts
Authors: Chuan Wen, Jianing Qian, Jierui Lin, Dinesh Jayaraman, Yang Gao
Date: 2021-09-29 00:00:00
Tasks: Autonomous Driving, continuous-control, Continuous Control, Decision Making, Imitation Learning, MuJoCo

When operating in partially observed settings, it is important for a control policy to fuse information from a history of observations.  However, a naive implementation of this approach has been observed repeatedly to fail for imitation-learned policies, often in surprising ways, and to the point of sometimes performing worse than when using instantaneous observations alone. We observe that behavioral cloning policies acting on single observations and observation histories each have their strengths and drawbacks, and combining them optimally could achieve the best of both worlds. Motivated by this, we propose a simple model combination approach inspired by human decision making:  we first compute a coarse action based on the instantaneous observation, and then refine it into a final action using historical information. Our experiments show that this outperforms all baselines on CARLA autonomous driving from images and various MuJoCo continuous control tasks.
Understanding the Success of Knowledge Distillation -- A Data Augmentation Perspective
Authors: Huan Wang, Suhas Lohit, Michael Jeffrey Jones, Yun Fu
Date: 2021-09-29 00:00:00
Tasks: Active Learning, Data Augmentation, Knowledge Distillation

Knowledge distillation (KD) is a general neural network training approach that uses a teacher model to guide a student model. Many works have explored the rationale for its success. However, its interplay with data augmentation (DA) has not been well understood so far. In this paper, we are motivated by an interesting observation in classification: KD loss can take more advantage of a DA method than cross-entropy loss \emph{simply by training for more iterations}. We present a generic framework to explain this interplay between KD and DA. Inspired by it, we enhance KD via stronger data augmentation schemes named TLmixup and TLCutMix. Furthermore, an even stronger and efficient DA approach is developed specifically for KD based on the idea of active learning. The findings and merits of our method are validated with extensive experiments on CIFAR-100, Tiny ImageNet, and ImageNet datasets. We achieve new state-of-the-art accuracy by using the original KD loss armed with stronger augmentation schemes, compared to existing state-of-the-art methods that employ more advanced distillation losses. We also show that, by combining our approaches with the advanced distillation losses, we can advance the state-of-the-art even further. In addition to very promising performance, this paper importantly sheds light on explaining the success of knowledge distillation. The interaction of KD and DA methods we have discovered can inspire more powerful KD algorithms.
How Much Can CLIP Benefit Vision-and-Language Tasks?
Authors: Anonymous
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022
Tasks: Question Answering, Visual Entailment, Visual Question Answering, Visual Question Answering (VQA)

Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to perceive the visual world. However, it has been observed that large-scale pretraining usually can result in better generalization performance, e.g., CLIP (Contrastive Language-Image Pre-training), trained on a massive amount of image-caption pairs, has shown a strong zero-shot capability on various vision tasks. To further study the advantage brought by CLIP, we propose to use CLIP as the visual encoder in various V&L models in two typical scenarios: 1) plugging CLIP into task-specific fine-tuning; 2) combining CLIP with V&L pre-training and transferring to downstream tasks. We show that CLIP significantly outperforms widely-used visual encoders trained with in-domain annotated data, such as BottomUp-TopDown. We achieve competitive or better results on diverse V&L tasks, while establishing new state-of-the-art results on Visual Question Answering, Visual Entailment, and V&L Navigation tasks.
EXACT: Scalable Graph Neural Networks Training via Extreme Activation Compression
Authors: Zirui Liu, Kaixiong Zhou, Fan Yang, Li Li, Rui Chen, Xia Hu
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: GPU, Graph Learning

Training Graph Neural Networks (GNNs) on large graphs is a fundamental challenge due to the high memory usage, which is mainly occupied by activations (e.g., node embeddings). Previous works usually focus on reducing the number of nodes retained in memory. In parallel, unlike what has been developed for other types of neural networks, training with compressed activation maps is less explored for GNNs. This extension is notoriously difficult to implement due to the miss of necessary tools in common graph learning packages. To unleash the potential of this direction, we provide a optimized GPU implementation which supports training GNNs with compressed activations. Based on the implementation, we propose a memory-efficient framework called ``EXACT'', which for the first time demonstrate the potential and evaluate the feasibility of training GNNs with compressed activations. We systematically analyze the trade-off among the memory saving, time overhead, and accuracy drop. In practice, EXACT can reduce the memory footprint of activations by up to $32\times$ with $0.2$-$0.5\%$ accuracy drop and $10$-$25\%$ time overhead across different models and datasets. In addition, EXACT can trim down the hardware requirement of training a three-layer full-batch GraphSAGE on \textit{ogbn-products} from a 48GB GPU to a 12GB GPU.
Sequential Covariate Shift Detection Using Classifier Two-Sample Tests
Authors: Sooyong Jang, Sangdon Park, Insup Lee, Osbert Bastani
Date: 2021-09-29 00:00:00
Tasks: Vocal Bursts Valence Prediction

A standard assumption in supervised learning is that the training data and test data are from the same distribution. However, this assumption often fails to hold in practice, which can cause the learned model to perform poorly. We consider the problem of detecting covariate shift, where the covariate distribution shifts but the conditional distribution of labels given covariates remains the same. This problem can naturally be solved using a two-sample test--- i.e., test whether the current test distribution of covariates equals the training distribution of covariates. Our algorithm builds on classifier tests, which train a discriminator to distinguish train and test covariates, and then use the accuracy of this discriminator as a test statistic. A key challenge is that classifier tests assume given a fixed set of test covariates. In practice, test covariates often arrive sequentially over time---e.g., a self-driving car observes a stream of images while driving. Furthermore, covariate shift can occur multiple times--- i.e., shift and then shift back later or gradually shift over time. To address these challenges, our algorithm trains the discriminator online. Furthermore, it evaluates test accuracy using each new covariate before taking a gradient step; this strategy avoids constructing a held-out test set, which can reduce sample efficiency. We prove that this optimization preserves the correctness---i.e., our algorithm achieves a desired bound on the false positive rate. In our experiments, we show that our algorithm efficiently detects covariate shifts on ImageNet.
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension
Authors: Paris Giampouras, Benjamin David Haeffele, Rene Vidal
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Representation Learning

Robust subspace recovery (RSR) is  a fundamental problem in robust representation learning. Although RSR has received considerable attention in the literature, there are still several aspects of RSR that remain highly unexplored. 
In particular, when the dimension of the underlying subspace is unknown and the data contains significant numbers of outlying entries many methods can struggle to identify the correct subspace. Here we focus on a recently proposed RSR method termed Dual Principal Component Pursuit (DPCP) approach, which aims to recover a basis of the orthogonal complement of the subspace.   While prior work has shown that DPCP can provably recover the correct subspace in the presence of outliers, this relies on knowing the true dimension of the subspace, which is typically not possible in practice, and DPCP often fails when this dimension is unknown.  Instead, we propose a very simple algorithm based on running multiple instances of a projected sub-gradient descent method (PSGM), with each problem instance seeking to find one vector in the null space of the subspace.  Here we show that under mild conditions that this approach will succeed with high probability.  In particular, we show that 1) all of the problem instances will  converge to a vector in the null space of the subspace and 2) the ensemble of problem instance solutions will be sufficiently diverse to fully span the null space of the subspace (and thus reveal the true codimension of the subspace) even when the true subspace dimension is unknown. We provide empirical results that corroborate our theoretical results and showcase the remarkable implicit rank regularization behavior of PSGM algorithm that allows us to perform RSR without being aware of the subspace dimension.
Was my Model Stolen? Feature Sharing for Robust and Transferable Watermarks
Authors: Ruixiang Tang, Hongye Jin, Curtis Wigington, Mengnan Du, Rajiv Jain, Xia Hu
Date: 2021-09-29 00:00:00
Tasks: Model extraction

Deep Neural Networks (DNNs) are increasingly being deployed in cloud-based services via various APIs, e.g., prediction APIs. Recent studies show that these public APIs are vulnerable to the model extraction attack, where an adversary attempts to train a local copy of the private model using predictions returned by the API. Existing defenses mainly focus on perturbing prediction distribution to undermine the training objective of the attacker and thus inevitably impact the API utility. In this work, we extend the concept of watermarking to protect APIs. The main idea is to insert a watermark which is only known to defender into the protected model and the watermark will then be transferred into all stolen models. The defender can leverage the knowledge of watermarks to detect and certify stolen models. However, the effectiveness of the watermark remains limited since watermarks are distinct from the task data, and the adversary in extraction attacks only adopts inputs sampled from the task distribution. Hence the watermark tends to be discarded during the extraction attack. To bridge the gap, we propose a feature-sharing framework to improve the transferability of watermarks. For legitimate data and watermarks, we encourage the model to only show the difference in final decision layers and use the same features for all other layers. Comprehensive experiments on text and image domains indicate that the proposed framework is effective in terms of API watermarking while keeping the utility of the API. Besides, experimental analysis also validates the robustness of the watermark against various watermark removal attacks.
Occupy & Specify: Investigations into a Maximum Credit Assignment Occupancy Objective for Data-efficient Reinforcement Learning
Authors: Emmanuel Daucé
Date: 2021-09-29 00:00:00

The capability to widely sample the state and action spaces is a key ingredient toward building effective reinforcement learning algorithms. The trade-off between exploration and exploitation generally requires the use of a data model, from which novelty bonuses are estimated and used to bias the return toward wider exploration. Surprisingly, little is known about the optimization objective followed when novelty (or entropy) bonuses are considered. Following the ``probability matching'' principle, we interpret here returns (cumulative rewards) as set points that fixate the occupancy of the state space, that is the frequency at which the different states are expected to be visited during trials. The circular dependence of the rewards sampling on the occupancy/policy makes it difficult to evaluate. We provide here a variational formulation for the matching objective, named MaCAO (Maximal Credit Assignment Occupancy) that interprets rewards as a log-likelihood on occupancy, that operates anticausally from the effects toward the causes. It is, broadly speaking, an estimation of the contribution of a state toward reaching a (future) goal. It is constructed so as to provide better convergence guaranties, with a complementary term serving as a regularizer, that, in principle, may reduce the greediness. In the absence of an explicit target occupancy, a uniform prior is used, making the regularizer consistent with a MaxEnt (Maximum Entropy) objective on states. Optimizing the entropy on states in known to be more tricky than optimizing the entropy on actions, because of an external sampling through the (unknown) environment, that prevents the propagation of a gradient. In our practical implementations, the MaxEnt regularizer is interpreted as a TD-error rather than a reward, making it possible to define an update in both the discrete and continuous cases. It is implemented on an actor-critic off-policy setup with a replay buffer, using gradient descent on a multi-layered neural network, and shown to provide significant increase in the sampling efficacy, that reflects in a reduced training time and higher returns on a set of classical motor learning benchmarks, in both the dense and the sparse rewards cases.