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2007.02617
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Understanding and Improving Fast Adversarial Training
6 July 2020
Maksym Andriushchenko
Nicolas Flammarion
AAML
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Papers citing
"Understanding and Improving Fast Adversarial Training"
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Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
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328
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Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses
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Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization
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697
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23 Dec 2021
ℓ
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199
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289
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Local Linearity and Double Descent in Catastrophic Overfitting
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61
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Robust and Accurate Object Detection via Self-Knowledge Distillation
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Pengzhi Chu
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149
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201
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Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks
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J. H. Metzen
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413
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M. P. Kumar
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132
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Jiqiang Liu
Xiaolin Chang
Jianhua Wang
Ricardo J. Rodríguez
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193
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Boosting Fast Adversarial Training with Learnable Adversarial Initialization
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Yong Zhang
Baoyuan Wu
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Xiaochun Cao
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311
65
0
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Vlado Menkovski
Yulong Pei
Mykola Pechenizkiy
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224
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Yichi Zhang
Chuan Guo
Zhiru Zhang
G. E. Suh
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223
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SoK: Machine Learning Governance
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Hengrui Jia
Anvith Thudi
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271
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139
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Navid Kardan
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479
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Single-Step Adversarial Training for Semantic Segmentation
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135
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Multi-stage Optimization based Adversarial Training
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114
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Probabilistic Margins for Instance Reweighting in Adversarial Training
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Yifan Zhang
Bo Han
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Chen Gong
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Mingyuan Zhou
Masashi Sugiyama
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203
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RobustNav: Towards Benchmarking Robustness in Embodied Navigation
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Aniruddha Kembhavi
285
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Concurrent Adversarial Learning for Large-Batch Training
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Xiangning Chen
Minhao Cheng
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210
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Two Coupled Rejection Metrics Can Tell Adversarial Examples Apart
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Huishuai Zhang
Di He
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Hang Su
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217
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Deep Repulsive Prototypes for Adversarial Robustness
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153
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132
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121
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181
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328
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On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
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Kaidi Xu
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246
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