pwc-archive/papers-with-abstracts huggingface dataset:
Generating Realistic Physical Adversarial Examplesby Patch Transformer Network
Authors: Quanfu Fan, Kaidi Xu, Chun-Fu Chen, Sijia Liu, Gaoyuan Zhang, David Daniel Cox, Xue Lin
Date: 2021-09-29 00:00:00
Tasks: Object

Physical adversarial attacks apply carefully crafted adversarial perturbations onto real objects to maliciously alter the prediction of object classifiers or detectors. The current standard method for designing physical adversarial patches, i.e. Expectation over Transformations (EoT), simulates real-world environments by random physical transformations, resulting in adversarial examples far from satisfactory. To tackle this issue, we propose and develop a novel network to learn real-world physical transformations from data, including geometric transformation, printer color transformation and illumination adaption. Our approach produces realisticlooking adversarial examples and can be integrated into existing attack generation frameworks to generate adversarial patches effectively. We apply our approach to design adversarial T-shirts worn by moving people, one of the most challenging settings for physical attacks. Experiments show that our approach significantly outperforms the state of the arts when attacking DL-based object detectors in real life. Moreover, we build a first-kind-of adversarial T-shirts dataset to enable effective training of our approach and facilitate fair comparison on physical world attacks by considering a standard patch size, environment changes and object variances. Our code will be made publicly available.
Multi-Domain Self-Supervised Learning
Authors: Neha Mukund Kalibhat, Yogesh Balaji, C. Bayan Bruss, Soheil Feizi
Date: 2021-09-29 00:00:00
Tasks: Contrastive Learning, Representation Learning, Self-Supervised Learning

Contrastive self-supervised learning has recently gained significant attention owing to its ability to learn improved feature representations without the use of label information. Current contrastive learning approaches, however, are only effective when trained on a particular dataset, limiting their utility in diverse multi-domain settings. In fact, training these methods on a combination of several domains often degrades the quality of learned representations compared to the models trained on a single domain. In this paper, we propose a Multi-Domain Self-Supervised Learning (MDSSL) approach that can effectively perform representation learning on multiple, diverse datasets. In MDSSL, we propose a three-level hierarchical loss for measuring the agreement between augmented views of a given sample, agreement between samples within a dataset and agreement between samples across datasets. We show that MDSSL when trained on a mixture of CIFAR-10, STL-10, SVHN and CIFAR-100 produces powerful representations, achieving up to a $25\%$ increase in top-1 accuracy on a linear classifier compared to single-domain self-supervised encoders. Moreover, MDSSL encoders can generalize more effectively to unseen datasets compared to both single-domain and multi-domain baselines. MDSSL is also highly efficient in terms of the resource usage as it stores and trains a single model for multiple datasets leading up to $17\%$ reduction in training time. Finally, for multi-domain datasets where domain labels are unknown, we propose a modified approach that alternates between clustering and MDSSL. Thus, for diverse multi-domain datasets (even without domain labels), MDSSL provides an efficient and generalizable self-supervised encoder without sacrificing the quality of representations in individual domains.
Continual Learning with Filter Atom Swapping
Authors: Zichen Miao, Ze Wang, Wei Chen, Qiang Qiu
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Continual Learning

Continual learning is widely studied in recent years to resolve the \textit{catastrophic forgetting} of deep neural networks. In this paper, we first enforce a low-rank filter subspace by decomposing convolutional filters within each network layer over a small set of filter atoms. Then, we perform continual learning with filter atom swapping. In other words, we learn for each task a new filter subspace for each convolutional layer, i.e., hundreds of parameters as filter atoms, but keep subspace coefficients shared across tasks. By maintaining a small footprint memory of filter atoms, we can easily archive models for past tasks to avoid forgetting. The effectiveness of this simple scheme for continual learning is illustrated both empirically and theoretically. The proposed atom swapping framework further enables flexible and efficient model ensemble with members selected within task or across tasks to improve the performance in different continual learning settings. The proposed method can be applied to a wide range of optimization schemes and convolutional network structures. Being validated on multiple benchmark datasets, the proposed method outperforms the state-of-the-art methods in both accuracy and scalability.
Exploring Covariate and Concept Shift for Detection and Confidence Calibration of Out-of-Distribution Data
Authors: Junjiao Tian, Yen-Chang Hsu, Yilin Shen, Hongxia Jin, Zsolt Kira
Date: 2021-09-29 00:00:00
Tasks: Out of Distribution (OOD) Detection, Sensitivity

Moving beyond testing on in-distribution data, works on Out-of-Distribution (OOD) detection have recently increased in popularity. A recent attempt to categorize OOD data introduces the concept of near and far OOD detection. Specifically, prior works define characteristics of OOD data in terms of detection difficulty. We propose to characterize the spectrum of OOD data using two types of distribution shifts: covariate shift and concept shift, where covariate shift corresponds to change in style, e.g., noise, and concept shift indicates change in semantics. This characterization reveals that sensitivity to each type of shift is important to the detection and model calibration of OOD data. Consequently, we investigate score functions that capture sensitivity to each type of dataset shift and methods that improve them. To this end, we theoretically derive two score functions for OOD detection, the covariate shift score and concept shift score, based on the decomposition of KL-divergence for both scores, and propose a geometrically-inspired method (Geometric ODIN) to improve OOD detection under both shifts with only in-distribution data. Additionally, the proposed method naturally leads to an expressive post-hoc calibration function which yields state-of-the-art calibration performance on both in-distribution and out-of-distribution data. We are the first to propose a method that works well across both OOD detection and calibration, and under different types of shifts. Specifically, we improve the previous state-of-the-art OOD detection by relatively 7% AUROC on CIFAR100 vs. SVHN and achieve the best calibration performance of 0.084 Expected Calibration Error on the corrupted CIFAR100C dataset.
Can Label-Noise Transition Matrix Help to Improve Sample Selection and Label Correction?
Authors: Yu Yao, Xuefeng Li, Tongliang Liu, Alan Blair, Mingming Gong, Bo Han, Gang Niu, Masashi Sugiyama
Date: 2021-09-29 00:00:00
Tasks: Learning with noisy labels, Memorization

Existing methods for learning with noisy labels can be generally divided into two categories: (1) sample selection and label correction based on the memorization effect of neural networks; (2) loss correction with the transition matrix. So far, the two categories of methods have been studied independently because they are designed according to different philosophies, i.e., the memorization effect is a property of the neural networks independent of label noise while the transition matrix is exploited to model the distribution of label noise. In this paper, we take a first step in unifying these two paradigms by showing that modelling the distribution of label noise with the transition matrix can also help sample selection and label correction, which leads to better robustness against different types of noise. More specifically, we first train a network with the loss corrected by the transition matrix and then use the confidence of the estimated clean class posterior from the network to select and re-label instances. Our proposed method demonstrates strong robustness on multiple benchmark datasets under various types of noise.
Multi-Critic Actor Learning: Teaching RL Policies to Act with Style
Authors: Siddharth Mysore, George Cheng, Yunqi Zhao, Kate Saenko, Meng Wu
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

Using a single value function (critic) shared over multiple tasks in Actor-Critic multi-task reinforcement learning (MTRL) can result in negative interference between tasks, which can compromise learning performance. Multi-Critic Actor Learning (MultiCriticAL) proposes instead maintaining separate value-function estimators, i.e. critics, for each task being trained. This relaxes an assumption of continuity between task values and avoids interference between task-value estimates. Explicitly distinguishing between tasks also eliminates the need for critics to learn to do so. MultiCriticAL is tested in the context of multi-style learning, a special case of MTRL where agents are trained to behave with different distinct behavior styles, and yields up to 45% performance gains over the single-critic baselines and even successfully learns behavior styles in cases where single-critic approaches may simply fail to learn. As a further test of MultiCriticAL’s utility, it is tested on a simulation of EA’s UFC game, where our method enables a single policy function to learn and smoothly transition between multiple fighting styles.
Don’t throw away that linear head: Few-shot protein fitness prediction with generative models
Authors: Ben Krause, Nikhil Naik, Wenhao Liu, Ali Madani
Date: 2021-09-29 00:00:00
Tasks: Transfer Learning

Predicting the fitness, i.e. functional value, of a protein sequence is an important and challenging task in biology, particularly due to the scarcity of assay-labeled data. Traditional approaches utilize transfer learning from evolutionary data, yet discard useful information from a generative model’s learned probability distribution. We propose generative fitness fine-tuning, termed gf-tuning, to utilize the generative model’s log probabilities as logits for a pairwise ranking loss---allowing for the full distribution learned in unsupervised training to be repurposed for fine-tuning on assay-labeled fitness data. We demonstrate that gf-tuning achieves better performance than existing baselines across a variety of few-shot fitness prediction settings, including both low homology and highly epistatic systems as well as generalizing from single to multiple mutations. Generative fitness finetuning offers an effective strategy for few-shot fitness prediction which could enable advances to better understand and engineer proteins.
Generate, Annotate, and Learn: Generative Models Advance Self-Training and Knowledge Distillation
Authors: Xuanli He, Islam Nassar, Jamie Ryan Kiros, Gholamreza Haffari, Mohammad Norouzi
Date: 2021-09-29 00:00:00
Tasks: Few-Shot Learning, Knowledge Distillation, Language Modeling, Language Modelling, Large Language Model

Semi-Supervised Learning (SSL) has seen success in many application domains, but this success often relies on the availability of task-specific unlabeled data. Knowledge distillation (KD) has enabled compressing deep networks, achieving the best results when distilling knowledge on fresh task-specific unlabeled examples. However, task-specific unlabeled data can be challenging to find, especially for NLP problems. We present a simple framework called "generate, annotate, and learn (GAL)" that uses unconditional language models to synthesize in-domain unlabeled data, helping advance SSL and KD on NLP and tabular tasks. To obtain strong task-specific generative models, we either fine-tune a large language model (LLM) on inputs from specific tasks, or prompt a LLM with a few input examples to generate more unlabeled examples. Then, we use existing classifiers to annotate generated unlabeled examples with pseudo labels, which are used as additional training data or as additional prompts. GAL improves prompt-based few-shot learning on several NLP tasks. It also yields a new state-of-the-art for 6-layer transformers on the GLUE leaderboard. Finally, self-training with GAL offers large gains on four tabular tasks from the UCI repository.
A NEW BACKBONE FOR HYPERSPECTRAL IMAGE RECONSTRUCTION
Authors: Jiamian Wang, Yulun Zhang, Xin Yuan, Yun Fu, Zhiqiang Tao
Date: 2021-09-29 00:00:00
Tasks: Computational Efficiency, Image Reconstruction

As the inverse process of snapshot compressive imaging, the hyperspectral image (HSI) reconstruction takes the 2D measurement as input and posteriorly retrieves the captured 3D spatial-spectral signal. Built upon several assumptions, numerous sophisticated neural networks have come to the fore in this task. Despite their prosperity under experimental settings, it's still extremely challenging for existing networks to achieve high-fidelity reconstructive quality while maximizing the reconstructive efficiency (computational efficiency and power occupation), which prohibits their further deployment in practical applications. In this paper, we firstly conduct a retrospective analysis on aforementioned assumptions, through which we indicate the imminent aspiration for an authentically practical-oriented network in reconstructive community. By analysing the effectiveness and limitations of the widely-used reconstructive backbone U-Net, we propose a Simple Reconstruction Network, namely SRN, just based on some popular techniques, e.g., scale/spectral-invariant learning and identity connection. It turns out, under current conditions, such a pragmatic solution outperforms existing reconstructive methods by an obvious margin and maximize the reconstructive efficiency concretely. We hope the proposed SRN can further contribute to the cutting-edge reconstructive methods as a promising backbone, and also benefit the realistic tasks, i.e., real-time/high-resolution HSI reconstruction, solely as a baseline.
Updater-Extractor Architecture for Inductive World State Representations
Authors: Arsenii Kirillovich Moskvichev, James A Liu
Date: 2021-09-29 00:00:00
Tasks: LEMMA, Pathfinder

Developing sequential models traditionally involves two stages - training and application. Retention of information acquired after training (at application time) is architecturally limited by the size of the model's context window (in the case of transformers), or by the practical difficulties associated with long sequences (in the case of RNNs). In this paper, we propose a novel transformer-based Updater-Extractor architecture that can work with sequences of arbitrary length and refine its long-term knowledge about the world based on inputs at application time. We explicitly train the model to incorporate incoming information into its world state representation, obtaining strong inductive generalization and the ability to handle extremely long-range dependencies. We propose a novel one-step training procedure that makes such training feasible, and prove a lemma that provides theoretical justification for this training procedure. Empirically, we investigate the model performance on a variety of different tasks: we use two new simulated tasks tasks to study the model's ability to handle extremely long-range dependencies, we demonstrate competitive performance on the challenging Pathfinder problem using vanilla attention.
On the Implicit Biases of Architecture & Gradient Descent
Authors: Jeremy Bernstein, Yisong Yue
Date: 2021-09-29 00:00:00
Tasks: Bayesian Inference

Do neural networks generalise because of bias in the functions returned by gradient descent, or bias already present in the network architecture? $\textit{¿Por qué no los dos?}$ This paper finds that while typical networks that fit the training data already generalise fairly well, gradient descent can further improve generalisation by selecting networks with a large margin. This conclusion is based on a careful study of the behaviour of infinite width networks trained by Bayesian inference and finite width networks trained by gradient descent. To measure the implicit bias of architecture, new technical tools are developed to both $\textit{analytically bound}$ and $\textit{consistently estimate}$ the average test error of the neural network--Gaussian process (NNGP) posterior. This error is found to be already better than chance, corroborating the findings of Valle-Pérez et al. (2019) and underscoring the importance of architecture. Going beyond this result, this paper finds that test performance can be substantially improved by selecting a function with much larger margin than is typical under the NNGP posterior. This highlights a curious fact: $\textit{minimum a posteriori}$ functions can generalise best, and gradient descent can select for those functions. In summary, new technical tools suggest a nuanced portrait of generalisation involving both the implicit biases of architecture and gradient descent.
AAVAE: Augmentation-Augmented Variational Autoencoders
Authors: William Alejandro Falcon, Ananya Harsh Jha, Teddy Koker, Kyunghyun Cho
Date: 2021-09-29 00:00:00
Tasks: Contrastive Learning, Data Augmentation, image-classification, Image Classification, Self-Supervised Learning

Recent methods for self-supervised learning can be grouped into two paradigms: contrastive and non-contrastive approaches. Their success can largely be attributed to data augmentation pipelines which generate multiple views of a single input that preserve the underlying semantics. In this work, we introduce augmentation-augmented variational autoencoders (AAVAE), yet another alternative to self-supervised learning, based on autoencoding. We derive AAVAE starting from the conventional variational autoencoder (VAE), by replacing the KL divergence regularization, which is agnostic to the input domain, with data augmentations that explicitly encourage the internal representations to encode domain-specific invariances and equivariances. We empirically evaluate the proposed AAVAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated. Our experiments confirm the effectiveness of data augmentation as a replacement for KL divergence regularization. The AAVAE outperforms the VAE by 30% on CIFAR-10, 40% on STL-10 and 45% on Imagenet. On CIFAR-10 and STL-10, the results for AAVAE are largely comparable to the state-of-the-art algorithms for self-supervised learning.
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations
Authors: Ke Sun, Yi Liu, Yingnan Zhao, Hengshuai Yao, Shangling Jui, Linglong Kong
Date: 2021-09-29 00:00:00
Tasks: Distributional Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse while training. In this paper, we study the training robustness of distributional Reinforcement Learning~(RL), a class of state-of-the-art methods that estimate the whole distribution, as opposed to only the expectation, of the total return. Firstly, we propose State-Noisy Markov Decision Process~(SN-MDP) in the tabular case to incorporate both random and adversarial state observation noises, in which the contraction of both expectation-based and distributional Bellman operators is derived. Beyond SN-MDP with the function approximation, we theoretically characterize the bounded gradient norm of histogram-based distributional loss, accounting for the better training robustness of distribution RL. We also provide stricter convergence conditions of the Temporal-Difference~(TD) learning under more flexible state noises, as well as the sensitivity analysis by the leverage of influence function. Finally, extensive experiments on the suite of games show that distributional RL enjoys better training robustness compared with its expectation-based counterpart across various state observation noises.
RoDesigner: Variation-Aware Optimization for Robust Analog Design with Multi-Task RL
Authors: Wei Shi, Hanrui Wang, Jiaqi Gu, Mingjie Liu, David Z. Pan, Song Han, Nan Sun
Date: 2021-09-29 00:00:00
Tasks: Bayesian Optimization

Analog/mixed-signal circuit design is one of the most complex and time-consuming stages in the chip design process. Due to various process, voltage, and temperature (PVT) variations from chip manufacturing, analog circuits inevitably suffer from performance degradations. Although there has been plenty of work on automating analog circuit design under the typical condition, limited research has been done on exploring robust designs under the real and unpredictable silicon variations.  To address these challenges, we present RoDesigner, a robust circuit design framework that involves the variation information in the optimization process. Specifically, circuit optimizations under different variations are considered as a set of tasks. Similarities among tasks are leveraged and competitions are alleviated to realize a sample-efficient multi-task training. Moreover, RoDesigner prunes the task space before multi-task training to reduce simulation costs. In this way, RoDesigner can rapidly produce a set of circuit parameters that satisfies diverse constraints (e.g., gain, bandwidth, noise...) across variations. We compare our method with Bayesian optimization, evolutionary algorithm, and Deep Deterministic Policy Gradient (DDPG) and demonstrate that RoDesigner can significantly reduce required optimization time by14×-30×.  We also show that RoDesigner’s circuit performance is as good as a state-of-the-art human design, while the design time is reduced from several days by an expert to an hour.
On the Expressiveness, Predictability and Interpretability of Neural Temporal Point Processes
Authors: Liangliang Shi, Fangyu Ding, Junchi Yan, Yanjie Duan, Guangjian Tian
Date: 2021-09-29 00:00:00
Tasks: Point Processes

Despite the fast advance in neural temporal point processes (NTPP) which enjoys high model capacity, there are still some standing gaps to fill including model expressiveness, predictability, and interpretability, especially with the wide application of event sequence modeling. For expressiveness, we first show the incapacity of existing NTPP models for fitting time-varying especially non-terminating TPP, and propose a simple neural model for expressive intensity function modeling. To improve predictability which is not directly optimized by the TPP likelihood objective, we devise our new sampling techniques that enable error metric driven adaptive fine-tuning of the sampling hyperparameter for predictive TPP, based on the event history in training sequences. Moreover, we show how interval-based event prediction can be achieved by our prediction techniques. To achieve interpretable NTPP, we propose an influence definition from one event to the future by comparing the difference between the existence of the event and not, which enables the dependency learning among events and types. Experimental results on synthetic datasets and public benchmarks show the efficacy of our approach.