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Improving Adversarial Robustness by Putting More Regularizations on Less
  Robust Samples

Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples

7 June 2022
Dongyoon Yang
Insung Kong
Yongdai Kim
    OOD
    AAML
ArXivPDFHTML

Papers citing "Improving Adversarial Robustness by Putting More Regularizations on Less Robust Samples"

5 / 5 papers shown
Title
TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification
TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification
Dongyoon Yang
Jihu Lee
Yongdai Kim
29
0
0
10 May 2025
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
Kasimir Tanner
Matteo Vilucchio
Bruno Loureiro
Florent Krzakala
AAML
63
0
0
31 Dec 2024
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training
Tejaswini Medi
Steffen Jung
M. Keuper
AAML
44
3
0
30 Oct 2024
ADBM: Adversarial diffusion bridge model for reliable adversarial purification
ADBM: Adversarial diffusion bridge model for reliable adversarial purification
Xiao-Li Li
Wenxuan Sun
Huanran Chen
Qiongxiu Li
Yining Liu
Yingzhe He
Jie Shi
Xiaolin Hu
AAML
68
8
0
01 Aug 2024
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
317
5,847
0
08 Jul 2016
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