Revisiting and Exploring Efficient Fast Adversarial Training via LAW:
Lipschitz Regularization and Auto Weight Averaging

Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging

22 August 2023

Jindong Gu

Papers citing "Revisiting and Exploring Efficient Fast Adversarial Training via LAW: Lipschitz Regularization and Auto Weight Averaging"

5 / 5 papers shown

Title
Mitigating the Backdoor Effect for Multi-Task Model Merging via Safety-Aware Subspace Jinluan Yang Anke Tang Didi Zhu Zhengyu Chen Li Shen Fei Wu MoMe AAML 62 3 0 17 Oct 2024
How Smooth Is Attention? Valérie Castin Pierre Ablin Gabriel Peyré AAML 40 9 0 22 Dec 2023
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training Pau de Jorge Adel Bibi Riccardo Volpi Amartya Sanyal Philip H. S. Torr Grégory Rogez P. Dokania AAML 51 45 0 02 Feb 2022
On the Convergence and Robustness of Adversarial Training Yisen Wang Xingjun Ma James Bailey Jinfeng Yi Bowen Zhou Quanquan Gu AAML 194 345 0 15 Dec 2021
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles Ranjie Duan Xingjun Ma Yisen Wang James Bailey •. A. K. Qin Yun Yang AAML 167 224 0 08 Mar 2020