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

14 March 2024

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 Kejia Zhang Juanjuan Weng Yuanzheng Cai Zhiming Luo Shaozi Li AAML 52 0 0 04 Jul 2024
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 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 Ziqiao Wang Yongyi Mao FedML MLT 32 22 0 07 Oct 2021