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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
Measure and Improve Robustness in NLP Models: A Survey Xuezhi Wang Haohan Wang Diyi Yang 300 139 0 15 Dec 2021
Triangle Attack: A Query-efficient Decision-based Adversarial Attack Xiaosen Wang Zeliang Zhang Kangheng Tong Dihong Gong Kun He Zhifeng Li Wei Liu AAML 92 62 0 13 Dec 2021
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses Chun Pong Lau Jiang-Long Liu Hossein Souri Wei-An Lin Soheil Feizi Ramalingam Chellappa AAML 81 13 0 12 Dec 2021
Mutual Adversarial Training: Learning together is better than going alone Jiang-Long Liu Chun Pong Lau Hossein Souri Soheil Feizi Ramalingam Chellappa OOD AAML 74 25 0 09 Dec 2021
Finding Deviated Behaviors of the Compressed DNN Models for Image Classifications Yongqiang Tian Wuqi Zhang Ming Wen Shing-Chi Cheung Chengnian Sun Shiqing Ma Yu Jiang 90 7 0 06 Dec 2021
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting Abdulmajid Murad F. Kraemer Kerstin Bach Gavin Taylor OOD BDL UQCV 40 12 0 05 Dec 2021
$$\ell_\infty$-Robustness and Beyond: Unleashing Efficient Adversarial Training$ $\ell_\infty$ -Robustness and Beyond: Unleashing Efficient Adversarial Training H. M. Dolatabadi S. Erfani C. Leckie OOD AAML 92 12 0 01 Dec 2021
Pyramid Adversarial Training Improves ViT Performance Charles Herrmann Kyle Sargent Lu Jiang Ramin Zabih Huiwen Chang Ce Liu Dilip Krishnan Deqing Sun ViT 116 59 0 30 Nov 2021
MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack Mengting Xu Tao Zhang Daoqiang Zhang AAML MedIm 79 25 0 29 Nov 2021
Subspace Adversarial Training Tao Li Yingwen Wu Sizhe Chen Kun Fang Xiaolin Huang AAML OOD 108 59 0 24 Nov 2021
Fooling Adversarial Training with Inducing Noise Zhirui Wang Yifei Wang Yisen Wang 73 14 0 19 Nov 2021
A Review of Adversarial Attack and Defense for Classification Methods Yao Li Minhao Cheng Cho-Jui Hsieh T. C. Lee AAML 71 69 0 18 Nov 2021
SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness Jongheon Jeong Sejun Park Minkyu Kim Heung-Chang Lee Do-Guk Kim Jinwoo Shin AAML 85 57 0 17 Nov 2021
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks Chen Ma Xiangyu Guo Li Chen Junhai Yong Yisen Wang AAML 80 16 0 15 Nov 2021
Understanding the Generalization Benefit of Model Invariance from a Data Perspective Sicheng Zhu Bang An Furong Huang 51 26 0 10 Nov 2021
Data Augmentation Can Improve Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 65 292 0 09 Nov 2021
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps Muhammad Awais Fengwei Zhou Chuanlong Xie Jiawei Li Sung-Ho Bae Zhenguo Li AAML 84 18 0 09 Nov 2021
Tightening the Approximation Error of Adversarial Risk with Auto Loss Function Search Pengfei Xia Ziqiang Li Bin Li AAML 121 3 0 09 Nov 2021
DeepSteal: Advanced Model Extractions Leveraging Efficient Weight Stealing in Memories Adnan Siraj Rakin Md Hafizul Islam Chowdhuryy Fan Yao Deliang Fan AAML MIACV 81 117 0 08 Nov 2021
Robust and Information-theoretically Safe Bias Classifier against Adversarial Attacks Lijia Yu Xiao-Shan Gao AAML 109 5 0 08 Nov 2021
Smooth Imitation Learning via Smooth Costs and Smooth Policies Sapana Chaudhary Balaraman Ravindran 48 1 0 03 Nov 2021
LTD: Low Temperature Distillation for Robust Adversarial Training Erh-Chung Chen Che-Rung Lee AAML 112 27 0 03 Nov 2021
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks Maksym Yatsura J. H. Metzen Matthias Hein OOD 93 14 0 02 Nov 2021
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds Yujia Huang Huan Zhang Yuanyuan Shi J Zico Kolter Anima Anandkumar 105 78 0 02 Nov 2021
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning? Lijie Fan Sijia Liu Pin-Yu Chen Gaoyuan Zhang Chuang Gan AAML VLM 95 124 0 01 Nov 2021
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach A. Sarkar Anirban Sarkar Sowrya Gali V. Balasubramanian AAML 52 7 0 30 Oct 2021
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks Hassan Dbouk Naresh R Shanbhag AAML 44 7 0 28 Oct 2021
Towards Evaluating the Robustness of Neural Networks Learned by Transduction Jiefeng Chen Xi Wu Yang Guo Yingyu Liang S. Jha ELM AAML 92 15 0 27 Oct 2021
RoMA: Robust Model Adaptation for Offline Model-based Optimization Sihyun Yu SungSoo Ahn Le Song Jinwoo Shin OffRL 95 36 0 27 Oct 2021
A Frequency Perspective of Adversarial Robustness Shishira R. Maiya Max Ehrlich Vatsal Agarwal Ser-Nam Lim Tom Goldstein Abhinav Shrivastava AAML 72 40 0 26 Oct 2021
Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples Y. Tan Penny Chong Jiamei Sun Ngai-Man Cheung Yuval Elovici Alexander Binder AAML 61 0 0 24 Oct 2021
How and When Adversarial Robustness Transfers in Knowledge Distillation? Rulin Shao Ming Zhou Cor-Paul Bezemer Cho-Jui Hsieh AAML 74 19 0 22 Oct 2021
Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff Adel Javanmard M. Mehrabi AAML 71 5 0 22 Oct 2021
Transductive Robust Learning Guarantees Omar Montasser Steve Hanneke Nathan Srebro 63 13 0 20 Oct 2021
A Regularization Method to Improve Adversarial Robustness of Neural Networks for ECG Signal Classification Linhai Ma Liang Liang 99 21 0 19 Oct 2021
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 122 302 0 18 Oct 2021
DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networks Yixiang Wang Jiqiang Liu Xiaolin Chang Jianhua Wang Ricardo J. Rodríguez AAML 95 31 0 14 Oct 2021
Parameterizing Activation Functions for Adversarial Robustness Sihui Dai Saeed Mahloujifar Prateek Mittal AAML 84 32 0 11 Oct 2021
Adversarial Token Attacks on Vision Transformers Ameya Joshi Gauri Jagatap Chinmay Hegde ViT 99 19 0 08 Oct 2021
Nonconvex-Nonconcave Min-Max Optimization with a Small Maximization Domain Dmitrii Ostrovskii Babak Barazandeh Meisam Razaviyayn 85 12 0 08 Oct 2021
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Hanxun Huang Yisen Wang S. Erfani Quanquan Gu James Bailey Xingjun Ma AAML TPM 139 102 0 07 Oct 2021
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision Julian Buchel Gregor Lenz Yalun Hu Sadique Sheik M. Sorbaro AAML 92 15 0 06 Oct 2021
Noisy Feature Mixup Soon Hoe Lim N. Benjamin Erichson Francisco Utrera Winnie Xu Michael W. Mahoney AAML 103 38 0 05 Oct 2021
Trustworthy AI: From Principles to Practices Yue Liu Peng Qi Bo Liu Shuai Di Jingen Liu Jiquan Pei Jinfeng Yi Bowen Zhou 213 383 0 04 Oct 2021
Calibrated Adversarial Training Tianjin Huang Vlado Menkovski Yulong Pei Mykola Pechenizkiy AAML 117 3 0 01 Oct 2021
Introducing the DOME Activation Functions Mohamed E. Hussein Wael AbdAlmageed 64 1 0 30 Sep 2021
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining Weizhe Hua Yichi Zhang Chuan Guo Zhiru Zhang G. E. Suh OOD 103 16 0 29 Sep 2021
Unsolved Problems in ML Safety Dan Hendrycks Nicholas Carlini John Schulman Jacob Steinhardt 285 294 0 28 Sep 2021
Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency Sohaib Kiani S. Awan Chao Lan Fengjun Li Bo Luo GAN AAML 44 7 0 25 Sep 2021
Adversarially Regularized Policy Learning Guided by Trajectory Optimization Zhigen Zhao Simiao Zuo T. Zhao Ye Zhao 79 10 0 16 Sep 2021