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Enhancing Adversarial Robustness in Low-Label Regime via Adaptively
  Weighted Regularization and Knowledge Distillation

Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation

8 August 2023
Dongyoon Yang
Insung Kong
Yongdai Kim
ArXivPDFHTML

Papers citing "Enhancing Adversarial Robustness in Low-Label Regime via Adaptively Weighted Regularization and Knowledge Distillation"

4 / 4 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
Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness
Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness
Kejia Zhang
Juanjuan Weng
Yuanzheng Cai
Zhiming Luo
Shaozi Li
AAML
59
0
0
04 Jul 2024
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo
  Labeling
FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
Bowen Zhang
Yidong Wang
Wenxin Hou
Hao Wu
Jindong Wang
Manabu Okumura
T. Shinozaki
AAML
226
862
0
15 Oct 2021
Adversarial examples in the physical world
Adversarial examples in the physical world
Alexey Kurakin
Ian Goodfellow
Samy Bengio
SILM
AAML
281
5,835
0
08 Jul 2016
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