Adversarial Examples Are Not Real Features

Adversarial Examples Are Not Real Features

29 October 2023

Papers citing "Adversarial Examples Are Not Real Features"

10 / 10 papers shown

Title
Unveiling and Mitigating Adversarial Vulnerabilities in Iterative Optimizers Elad Sofer Tomer Shaked Caroline Chaux Nir Shlezinger AAML 45 0 0 26 Apr 2025
Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective Yifei Wang Liangchen Li Jiansheng Yang Zhouchen Lin Yisen Wang 31 11 0 30 Oct 2023
Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism Zhijian Zhuo Yifei Wang Jinwen Ma Yisen Wang 48 25 0 04 Mar 2023
Rethinking the Effect of Data Augmentation in Adversarial Contrastive Learning Rundong Luo Yifei Wang Yisen Wang 34 25 0 02 Mar 2023
Can Adversarial Training Be Manipulated By Non-Robust Features? Lue Tao Lei Feng Hongxin Wei Jinfeng Yi Sheng-Jun Huang Songcan Chen AAML 107 16 0 31 Jan 2022
On the Convergence and Robustness of Adversarial Training Yisen Wang Xingjun Ma James Bailey Jinfeng Yi Bowen Zhou Quanquan Gu AAML 212 345 0 15 Dec 2021
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 322 7,457 0 11 Nov 2021
A Unified Game-Theoretic Interpretation of Adversarial Robustness Jie Ren Die Zhang Yisen Wang Lu Chen Zhanpeng Zhou ... Xu Cheng Xin Wang Meng Zhou Jie Shi Quanshi Zhang AAML 72 22 0 12 Mar 2021
Densely Connected Convolutional Networks Gao Huang Zhuang Liu L. V. D. van der Maaten Kilian Q. Weinberger PINN 3DV 315 36,381 0 25 Aug 2016
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 375 75,888 0 18 May 2015