NoiLIn: Improving Adversarial Training and Correcting Stereotype of
Noisy Labels

NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels

31 May 2021

Bo Han

Masashi Sugiyama

Papers citing "NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels"

8 / 8 papers shown

Title
Accurate Forgetting for Heterogeneous Federated Continual Learning Abudukelimu Wuerkaixi Sen Cui J. Zhang Kunda Yan Bo Han Gang Niu Lei Fang Changshui Zhang Masashi Sugiyama 110 8 0 20 Feb 2025
Fairness Improves Learning from Noisily Labeled Long-Tailed Data Jiaheng Wei Zhaowei Zhu Gang Niu Tongliang Liu Sijia Liu Masashi Sugiyama Yang Liu 30 6 0 22 Mar 2023
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
Adv-4-Adv: Thwarting Changing Adversarial Perturbations via Adversarial Domain Adaptation Tianyue Zheng Zhe Chen Shuya Ding Chao Cai Jun-Jie Luo AAML 33 5 0 01 Dec 2021
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Hanxun Huang Yisen Wang S. Erfani Quanquan Gu James Bailey Xingjun Ma AAML TPM 46 100 0 07 Oct 2021
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 225 676 0 19 Oct 2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee Hyungyu Lee Sungroh Yoon AAML 161 113 0 05 Mar 2020
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 186 272 0 03 Dec 2018