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Learn from the Past: A Proxy Guided Adversarial Defense Framework with
  Self Distillation Regularization

Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization

19 October 2023
Yaohua Liu
Jiaxin Gao
Xianghao Jiao
Zhu Liu
Xin-Yue Fan
Risheng Liu
    AAML
ArXivPDFHTML

Papers citing "Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization"

5 / 5 papers shown
Title
Label Noise in Adversarial Training: A Novel Perspective to Study Robust
  Overfitting
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting
Chengyu Dong
Liyuan Liu
Jingbo Shang
NoLa
AAML
48
18
0
07 Oct 2021
Meta Gradient Adversarial Attack
Meta Gradient Adversarial Attack
Zheng Yuan
Jie M. Zhang
Yunpei Jia
Chuanqi Tan
Tao Xue
Shiguang Shan
AAML
47
78
0
09 Aug 2021
RobustBench: a standardized adversarial robustness benchmark
RobustBench: a standardized adversarial robustness benchmark
Francesco Croce
Maksym Andriushchenko
Vikash Sehwag
Edoardo Debenedetti
Nicolas Flammarion
M. Chiang
Prateek Mittal
Matthias Hein
VLM
217
675
0
19 Oct 2020
There Are Many Consistent Explanations of Unlabeled Data: Why You Should
  Average
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
Ben Athiwaratkun
Marc Finzi
Pavel Izmailov
A. Wilson
194
243
0
14 Jun 2018
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
250
5,833
0
08 Jul 2016
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