Annealing Self-Distillation Rectification Improves Adversarial Training

Annealing Self-Distillation Rectification Improves Adversarial Training

20 May 2023

Papers citing "Annealing Self-Distillation Rectification Improves Adversarial Training"

10 / 10 papers shown

Title
New Paradigm of Adversarial Training: Breaking Inherent Trade-Off between Accuracy and Robustness via Dummy Classes Y. Wang Li Liu Zi Liang Qingqing Ye Haibo Hu AAML 18 0 0 16 Oct 2024
Adversarial Robustness Overestimation and Instability in TRADES Jonathan Weiping Li Ren-Wei Liang Cheng-Han Yeh Cheng-Chang Tsai Kuanchun Yu Chun-Shien Lu Shang-Tse Chen AAML 38 0 0 10 Oct 2024
Revisiting Semi-supervised Adversarial Robustness via Noise-aware Online Robust Distillation Tsung-Han Wu Hung-Ting Su Shang-Tse Chen Winston H. Hsu 18 1 0 19 Sep 2024
Efficient and Effective Augmentation Strategy for Adversarial Training Sravanti Addepalli Samyak Jain R. Venkatesh Babu AAML 60 58 0 27 Oct 2022
Alleviating Robust Overfitting of Adversarial Training With Consistency Regularization Shudong Zhang Haichang Gao Tianwei Zhang Yunyi Zhou Zihui Wu AAML 18 3 0 24 May 2022
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
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 300 5,761 0 29 Apr 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 217 674 0 19 Oct 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 268 5,652 0 05 Dec 2016
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 256 3,108 0 04 Nov 2016