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Theoretically Principled Trade-off between Robustness and Accuracy

24 January 2019

Papers citing "Theoretically Principled Trade-off between Robustness and Accuracy"

50 / 837 papers shown

Title
Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately Fereshte Khani Percy Liang FaML 61 66 0 07 Dec 2020
A Singular Value Perspective on Model Robustness Malhar Jere Maghav Kumar F. Koushanfar AAML 86 6 0 07 Dec 2020
Unsupervised Adversarially-Robust Representation Learning on Graphs Jiarong Xu Yang Yang Junru Chen Chunping Wang Xin Jiang Jiangang Lu Yizhou Sun SSL AAML OOD 169 38 0 04 Dec 2020
FAT: Federated Adversarial Training Giulio Zizzo Ambrish Rawat M. Sinn Beat Buesser FedML 86 43 0 03 Dec 2020
Learning from others' mistakes: Avoiding dataset biases without modeling them Victor Sanh Thomas Wolf Yonatan Belinkov Alexander M. Rush 96 116 0 02 Dec 2020
Adversarial Robustness Across Representation Spaces Pranjal Awasthi George Yu Chun-Sung Ferng Andrew Tomkins Da-Cheng Juan OOD AAML 81 11 0 01 Dec 2020
Robustness Out of the Box: Compositional Representations Naturally Defend Against Black-Box Patch Attacks Christian Cosgrove Adam Kortylewski Chenglin Yang Alan Yuille AAML 70 4 0 01 Dec 2020
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses Gaurang Sriramanan Sravanti Addepalli Arya Baburaj R. Venkatesh Babu AAML 82 95 0 30 Nov 2020
Voting based ensemble improves robustness of defensive models Devvrit Minhao Cheng Cho-Jui Hsieh Inderjit Dhillon OOD FedML AAML 66 12 0 28 Nov 2020
A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks Hao Shen Sihong Chen Ran Wang 70 5 0 27 Nov 2020
Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization T. Han S. Nebelung F. Pedersoli Markus Zimmermann M. Schulze-Hagen ... Christoph Haarburger Fabian Kiessling Christiane Kuhl Volkmar Schulz Daniel Truhn MedIm 25 36 0 25 Nov 2020
Augmented Lagrangian Adversarial Attacks Jérôme Rony Eric Granger M. Pedersoli Ismail Ben Ayed AAML 82 39 0 24 Nov 2020
Learnable Boundary Guided Adversarial Training Jiequan Cui Shu Liu Liwei Wang Jiaya Jia OOD AAML 113 132 0 23 Nov 2020
A Neuro-Inspired Autoencoding Defense Against Adversarial Perturbations Can Bakiskan Metehan Cekic Ahmet Dundar Sezer Upamanyu Madhow AAML 50 0 0 21 Nov 2020
Self-Gradient Networks Hossein Aboutalebi M. Shafiee AAML 27 0 0 18 Nov 2020
Shaping Deep Feature Space towards Gaussian Mixture for Visual Classification Weitao Wan Jiansheng Chen Cheng Yu Tong Wu Yuanyi Zhong Ming-Hsuan Yang 36 7 0 18 Nov 2020
Extreme Value Preserving Networks Mingjie Sun Jianguo Li Changshui Zhang AAML MDE 23 0 0 17 Nov 2020
Towards Understanding the Regularization of Adversarial Robustness on Neural Networks Yuxin Wen Shuai Li Kui Jia AAML 72 24 0 15 Nov 2020
SALAD: Self-Assessment Learning for Action Detection Guillaume Vaudaux-Ruth Adrien Chan-Hon-Tong Catherine Achard 39 8 0 13 Nov 2020
Adversarial Image Color Transformations in Explicit Color Filter Space Zhengyu Zhao Zhuoran Liu Martha Larson AAML 110 14 0 12 Nov 2020
Fooling the primate brain with minimal, targeted image manipulation Li-xin Yuan Will Xiao Giorgia Dellaferrera Gabriel Kreiman Francis E. H. Tay Jiashi Feng Margaret Livingstone AAML 38 1 0 11 Nov 2020
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks Tao Bai Jinqi Luo Jun Zhao AAML 87 8 0 03 Nov 2020
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems Jiawei Zhang Peijun Xiao Ruoyu Sun Zhi-Quan Luo 120 99 0 29 Oct 2020
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy Philipp Benz Chaoning Zhang Adil Karjauv In So Kweon AAML 87 59 0 26 Oct 2020
Robust Pre-Training by Adversarial Contrastive Learning Ziyu Jiang Tianlong Chen Ting-Li Chen Zhangyang Wang 108 234 0 26 Oct 2020
Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks Huimin Zeng Chen Zhu Tom Goldstein Furong Huang AAML 72 18 0 24 Oct 2020
Posterior Differential Regularization with f-divergence for Improving Model Robustness Hao Cheng Xiaodong Liu L. Pereira Yaoliang Yu Jianfeng Gao 303 31 0 23 Oct 2020
Contrastive Learning with Adversarial Examples Chih-Hui Ho Nuno Vasconcelos SSL 92 142 0 22 Oct 2020
Maximum Mean Discrepancy Test is Aware of Adversarial Attacks Ruize Gao Feng Liu Jingfeng Zhang Bo Han Tongliang Liu Gang Niu Masashi Sugiyama AAML 95 58 0 22 Oct 2020
Precise Statistical Analysis of Classification Accuracies for Adversarial Training Adel Javanmard Mahdi Soltanolkotabi AAML 107 63 0 21 Oct 2020
Robust Neural Networks inspired by Strong Stability Preserving Runge-Kutta methods Byungjoo Kim Bryce Chudomelka Jinyoung Park Jaewoo Kang Youngjoon Hong Hyunwoo J. Kim AAML 54 6 0 20 Oct 2020
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 355 707 0 19 Oct 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 121 48 0 19 Oct 2020
Weight-Covariance Alignment for Adversarially Robust Neural Networks Panagiotis Eustratiadis Henry Gouk Da Li Timothy M. Hospedales OOD AAML 86 23 0 17 Oct 2020
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning Hongjun Wang Guanbin Li Xiaobai Liu Liang Lin GAN AAML 95 23 0 15 Oct 2020
To be Robust or to be Fair: Towards Fairness in Adversarial Training Han Xu Xiaorui Liu Yaxin Li Anil K. Jain Jiliang Tang 70 181 0 13 Oct 2020
Improve Adversarial Robustness via Weight Penalization on Classification Layer Cong Xu Dan Li Min Yang AAML 24 4 0 08 Oct 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 73 331 0 07 Oct 2020
Constraining Logits by Bounded Function for Adversarial Robustness Sekitoshi Kanai Masanori Yamada Shin'ya Yamaguchi Hiroshi Takahashi Yasutoshi Ida AAML 28 4 0 06 Oct 2020
Understanding Catastrophic Overfitting in Single-step Adversarial Training Hoki Kim Woojin Lee Jaewook Lee AAML 134 112 0 05 Oct 2020
Geometry-aware Instance-reweighted Adversarial Training Jingfeng Zhang Jianing Zhu Gang Niu Bo Han Masashi Sugiyama Mohan Kankanhalli AAML 94 279 0 05 Oct 2020
Do Wider Neural Networks Really Help Adversarial Robustness? Boxi Wu Jinghui Chen Deng Cai Xiaofei He Quanquan Gu AAML 110 95 0 03 Oct 2020
Efficient Robust Training via Backward Smoothing Jinghui Chen Yu Cheng Zhe Gan Quanquan Gu Jingjing Liu AAML 83 40 0 03 Oct 2020
Bag of Tricks for Adversarial Training Tianyu Pang Xiao Yang Yinpeng Dong Hang Su Jun Zhu AAML 90 270 0 01 Oct 2020
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients Yifei Huang Yaodong Yu Hongyang R. Zhang Yi-An Ma Yuan Yao AAML 84 27 0 28 Sep 2020
A Unifying Review of Deep and Shallow Anomaly Detection Lukas Ruff Jacob R. Kauffmann Robert A. Vandermeulen G. Montavon Wojciech Samek Marius Kloft Thomas G. Dietterich Klaus-Robert Muller UQCV 148 806 0 24 Sep 2020
Feature Distillation With Guided Adversarial Contrastive Learning Tao Bai Jinnan Chen Jun Zhao Bihan Wen Xudong Jiang Alex C. Kot AAML 58 9 0 21 Sep 2020
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness Tuan-Anh Bui Trung Le He Zhao Paul Montague O. deVel Tamas Abraham Dinh Q. Phung AAML FedML 73 11 0 21 Sep 2020
Adversarial Training with Stochastic Weight Average Joong-won Hwang Youngwan Lee Sungchan Oh Yuseok Bae OOD AAML 65 11 0 21 Sep 2020
Encoding Robustness to Image Style via Adversarial Feature Perturbations Manli Shu Zuxuan Wu Micah Goldblum Tom Goldstein AAML OOD 75 19 0 18 Sep 2020