Attacks Which Do Not Kill Training Make Adversarial Learning Stronger

26 February 2020

Bo Han

Papers citing "Attacks Which Do Not Kill Training Make Adversarial Learning Stronger"

30 / 80 papers shown

Title
Sparsity Winning Twice: Better Robust Generalization from More Efficient Training Tianlong Chen Zhenyu (Allen) Zhang Pengju Wang Santosh Balachandra Haoyu Ma Zehao Wang Zhangyang Wang OOD AAML 77 46 0 20 Feb 2022
Layer-wise Regularized Adversarial Training using Layers Sustainability Analysis (LSA) framework Mohammad Khalooei M. Homayounpour M. Amirmazlaghani AAML 17 3 0 05 Feb 2022
Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks Simone Marullo Matteo Tiezzi Marco Gori S. Melacci AAML GAN 19 2 0 18 Dec 2021
On the Convergence and Robustness of Adversarial Training Yisen Wang Xingjun Ma James Bailey Jinfeng Yi Bowen Zhou Quanquan Gu AAML 192 345 0 15 Dec 2021
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness Konstantinos P. Panousis S. Chatzis Sergios Theodoridis BDL AAML 60 11 0 05 Dec 2021
Subspace Adversarial Training Tao Li Yingwen Wu Sizhe Chen Kun Fang Xiaolin Huang AAML OOD 36 56 0 24 Nov 2021
LTD: Low Temperature Distillation for Robust Adversarial Training Erh-Chung Chen Che-Rung Lee AAML 24 26 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 24 14 0 02 Nov 2021
AugMax: Adversarial Composition of Random Augmentations for Robust Training Haotao Wang Chaowei Xiao Jean Kossaifi Zhiding Yu Anima Anandkumar Zhangyang Wang 19 106 0 26 Oct 2021
Parameterizing Activation Functions for Adversarial Robustness Sihui Dai Saeed Mahloujifar Prateek Mittal AAML 42 32 0 11 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 44 100 0 07 Oct 2021
Local Intrinsic Dimensionality Signals Adversarial Perturbations Sandamal Weerasinghe T. Alpcan S. Erfani C. Leckie Benjamin I. P. Rubinstein AAML 15 0 0 24 Sep 2021
Regional Adversarial Training for Better Robust Generalization Chuanbiao Song Yanbo Fan Yichen Yang Baoyuan Wu Yiming Li Zhifeng Li Kun He AAML OOD 11 6 0 02 Sep 2021
Neural Architecture Dilation for Adversarial Robustness Yanxi Li Zhaohui Yang Yunhe Wang Chang Xu AAML 22 23 0 16 Aug 2021
Imbalanced Adversarial Training with Reweighting Wentao Wang Han Xu Xiaorui Liu Yaxin Li B. Thuraisingham Jiliang Tang 29 16 0 28 Jul 2021
SemEval-2021 Task 11: NLPContributionGraph -- Structuring Scholarly NLP Contributions for a Research Knowledge Graph Jennifer D'Souza Sören Auer Ted Pedersen 36 30 0 10 Jun 2021
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks Leo Schwinn René Raab A. Nguyen Dario Zanca Bjoern M. Eskofier AAML 14 58 0 21 May 2021
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 24 65 0 09 Apr 2021
On the Robustness of Vision Transformers to Adversarial Examples Kaleel Mahmood Rigel Mahmood Marten van Dijk ViT 20 217 0 31 Mar 2021
Dynamic Efficient Adversarial Training Guided by Gradient Magnitude Fu Lee Wang Yanghao Zhang Yanbin Zheng Wenjie Ruan 23 1 0 04 Mar 2021
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training Dorjan Hitaj Giulio Pagnotta I. Masi L. Mancini OOD AAML 16 22 0 02 Mar 2021
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs S. Feizi AAML 32 45 0 15 Feb 2021
Guided Interpolation for Adversarial Training Chen Chen Jingfeng Zhang Xilie Xu Tianlei Hu Gang Niu Gang Chen Masashi Sugiyama AAML 16 10 0 15 Feb 2021
A Survey of Label-noise Representation Learning: Past, Present and Future Bo Han Quanming Yao Tongliang Liu Gang Niu Ivor W. Tsang James T. Kwok Masashi Sugiyama NoLa 24 158 0 09 Nov 2020
Robust Pre-Training by Adversarial Contrastive Learning Ziyu Jiang Tianlong Chen Ting-Li Chen Zhangyang Wang 16 226 0 26 Oct 2020
Geometry-aware Instance-reweighted Adversarial Training Jingfeng Zhang Jianing Zhu Gang Niu Bo Han Masashi Sugiyama Mohan S. Kankanhalli AAML 28 269 0 05 Oct 2020
Adversarially Robust Neural Architectures Minjing Dong Yanxi Li Yunhe Wang Chang Xu AAML OOD 34 48 0 02 Sep 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 18 18 0 19 May 2020
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization Yifei Min Lin Chen Amin Karbasi AAML 26 69 0 25 Feb 2020
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models Lin Chen Yifei Min Mingrui Zhang Amin Karbasi OOD 24 64 0 11 Feb 2020