Less is More: Data Pruning for Faster Adversarial Training

23 February 2023

Papers citing "Less is More: Data Pruning for Faster Adversarial Training"

20 / 20 papers shown

Title
Beyond ImageNet Attack: Towards Crafting Adversarial Examples for Black-box Domains Qilong Zhang Xiaodan Li YueFeng Chen Jingkuan Song Lianli Gao Yuan He Hui Xue AAML 83 65 0 27 Jan 2022
Improving language models by retrieving from trillions of tokens Sebastian Borgeaud A. Mensch Jordan Hoffmann Trevor Cai Eliza Rutherford ... Simon Osindero Karen Simonyan Jack W. Rae Erich Elsen Laurent Sifre KELM RALM 230 1,083 0 08 Dec 2021
A Survey of Modern Deep Learning based Object Detection Models Syed Sahil Abbas Zaidi M. S. Ansari Asra Aslam N. Kanwal M. Asghar Brian Lee VLM ObjD 117 752 0 24 Apr 2021
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training Krishnateja Killamsetty D. Sivasubramanian Ganesh Ramakrishnan A. De Rishabh K. Iyer OOD 127 201 0 27 Feb 2021
Sharpness-Aware Minimization for Efficiently Improving Generalization Pierre Foret Ariel Kleiner H. Mobahi Behnam Neyshabur AAML 184 1,345 0 03 Oct 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 213 1,842 0 03 Mar 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang Xilie Xu Bo Han Gang Niu Li-zhen Cui Masashi Sugiyama Mohan S. Kankanhalli AAML 56 404 0 26 Feb 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 261 833 0 19 Feb 2020
Self-training with Noisy Student improves ImageNet classification Qizhe Xie Minh-Thang Luong Eduard H. Hovy Quoc V. Le NoLa 304 2,387 0 11 Nov 2019
Sparse and Imperceivable Adversarial Attacks Francesco Croce Matthias Hein AAML 88 199 0 11 Sep 2019
Adversarial Training for Free! Ali Shafahi Mahyar Najibi Amin Ghiasi Zheng Xu John P. Dickerson Christoph Studer L. Davis Gavin Taylor Tom Goldstein AAML 125 1,245 0 29 Apr 2019
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks Aamir Mustafa Salman Khan Munawar Hayat Roland Göcke Jianbing Shen Ling Shao AAML 56 152 0 01 Apr 2019
Curriculum Adversarial Training Qi-Zhi Cai Min Du Chang-rui Liu D. Song AAML 72 162 0 13 May 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 205 3,184 0 01 Feb 2018
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser Fangzhou Liao Ming Liang Yinpeng Dong Tianyu Pang Xiaolin Hu Jun Zhu 83 886 0 08 Dec 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 287 12,060 0 19 Jun 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 247 8,550 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 517 5,897 0 08 Jul 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 350 10,180 0 16 Mar 2016
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 254 19,045 0 20 Dec 2014