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Soften to Defend: Towards Adversarial Robustness via Self-Guided Label
  Refinement

Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement

14 March 2024
Daiwei Yu
Zhuorong Li
Lina Wei
Canghong Jin
Yun Zhang
Sixian Chan
ArXivPDFHTML

Papers citing "Soften to Defend: Towards Adversarial Robustness via Self-Guided Label Refinement"

4 / 4 papers shown
Title
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
52
0
0
04 Jul 2024
Self-Improving Robust Preference Optimization
Self-Improving Robust Preference Optimization
Eugene Choi
Arash Ahmadian
Matthieu Geist
Oilvier Pietquin
M. G. Azar
23
8
0
03 Jun 2024
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
On the Generalization of Models Trained with SGD: Information-Theoretic
  Bounds and Implications
On the Generalization of Models Trained with SGD: Information-Theoretic Bounds and Implications
Ziqiao Wang
Yongyi Mao
FedML
MLT
32
22
0
07 Oct 2021
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