Revisiting Residual Networks for Adversarial Robustness: An Architectural Perspective

21 December 2022

Papers citing "Revisiting Residual Networks for Adversarial Robustness: An Architectural Perspective"

9 / 9 papers shown

Title
Provably Safeguarding a Classifier from OOD and Adversarial Samples: an Extreme Value Theory Approach Nicolas Atienza Christophe Labreuche Johanne Cohen Michele Sebag OODD AAML 57 0 0 20 Jan 2025
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Yong Xie Weijie Zheng Hanxun Huang Guangnan Ye Xingjun Ma AAML 61 1 0 20 Nov 2024
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization) Zhenyu Zhu Fanghui Liu Grigorios G. Chrysos V. Cevher 24 19 0 15 Sep 2022
On the Convergence and Robustness of Adversarial Training Yisen Wang Xingjun Ma James Bailey Jinfeng Yi Bowen Zhou Quanquan Gu AAML 183 344 0 15 Dec 2021
Are Transformers More Robust Than CNNs? Yutong Bai Jieru Mei Alan Yuille Cihang Xie ViT AAML 167 256 0 10 Nov 2021
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Hanxun Huang Yisen Wang S. Erfani Quanquan Gu James Bailey Xingjun Ma AAML TPM 44 100 0 07 Oct 2021
ResNet strikes back: An improved training procedure in timm Ross Wightman Hugo Touvron Hervé Jégou AI4TS 198 477 0 01 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 207 668 0 19 Oct 2020
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Z. Tu Kaiming He 261 10,106 0 16 Nov 2016