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Intriguing properties of adversarial training at scale

10 June 2019

Cihang Xie

Alan Yuille

AAML

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Papers citing "Intriguing properties of adversarial training at scale"

38 / 38 papers shown

Title
Generative Adversarial Networks Gilad Cohen Raja Giryes GAN 298 30,152 0 01 Mar 2022
No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting with Adversarial Attacks Siqi Liu A. Setio Florin-Cristian Ghesu Eli Gibson Sasa Grbic Bogdan Georgescu Dorin Comaniciu AAML OOD 95 40 0 08 Mar 2020
Adversarial Examples Improve Image Recognition Cihang Xie Mingxing Tan Boqing Gong Jiang Wang Alan Yuille Quoc V. Le AAML 137 566 0 21 Nov 2019
Instance adaptive adversarial training: Improved accuracy tradeoffs in neural nets Yogesh Balaji Tom Goldstein Judy Hoffman AAML 193 103 0 17 Oct 2019
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training Haichao Zhang Jianyu Wang AAML 87 230 0 24 Jul 2019
Adversarial Robustness through Local Linearization Chongli Qin James Martens Sven Gowal Dilip Krishnan Krishnamurthy Dvijotham Alhussein Fawzi Soham De Robert Stanforth Pushmeet Kohli AAML 74 308 0 04 Jul 2019
Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty Dan Hendrycks Mantas Mazeika Saurav Kadavath Basel Alomair OOD SSL 62 950 0 28 Jun 2019
Defending Adversarial Attacks by Correcting logits Yifeng Li Lingxi Xie Ya Zhang Rui Zhang Yanfeng Wang Qi Tian AAML 39 5 0 26 Jun 2019
Convergence of Adversarial Training in Overparametrized Neural Networks Ruiqi Gao Tianle Cai Haochuan Li Liwei Wang Cho-Jui Hsieh Jason D. Lee AAML 103 109 0 19 Jun 2019
Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai Tianle Cai Di He Chen Dan Kun He John E. Hopcroft Liwei Wang 92 158 0 03 Jun 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 130 754 0 31 May 2019
Are Labels Required for Improving Adversarial Robustness? J. Uesato Jean-Baptiste Alayrac Po-Sen Huang Robert Stanforth Alhussein Fawzi Pushmeet Kohli AAML 85 335 0 31 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 95 1,845 0 06 May 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang Tianyuan Zhang Yiping Lu Zhanxing Zhu Bin Dong AAML 124 362 0 02 May 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 136 1,253 0 29 Apr 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 89 727 0 28 Jan 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 159 2,560 0 24 Jan 2019
Adversarial Robustness May Be at Odds With Simplicity Preetum Nakkiran AAML 90 108 0 02 Jan 2019
Feature Denoising for Improving Adversarial Robustness Cihang Xie Yuxin Wu Laurens van der Maaten Alan Yuille Kaiming He 128 912 0 09 Dec 2018
Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks Jianyu Wang Haichao Zhang OOD AAML 77 119 0 26 Nov 2018
Logit Pairing Methods Can Fool Gradient-Based Attacks Marius Mosbach Maksym Andriushchenko T. A. Trost Matthias Hein Dietrich Klakow AAML 68 83 0 29 Oct 2018
Evaluating and Understanding the Robustness of Adversarial Logit Pairing Logan Engstrom Andrew Ilyas Anish Athalye AAML 71 141 0 26 Jul 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 110 1,784 0 30 May 2018
Group Normalization Yuxin Wu Kaiming He 247 3,676 0 22 Mar 2018
Adversarial Logit Pairing Harini Kannan Alexey Kurakin Ian Goodfellow AAML 98 629 0 16 Mar 2018
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 319 12,151 0 19 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,729 0 19 May 2017
Detecting Adversarial Samples from Artifacts Reuben Feinman Ryan R. Curtin S. Shintre Andrew B. Gardner AAML 105 894 0 01 Mar 2017
On Detecting Adversarial Perturbations J. H. Metzen Tim Genewein Volker Fischer Bastian Bischoff AAML 77 950 0 14 Feb 2017
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics Xin Li Fuxin Li GAN AAML 115 366 0 22 Dec 2016
Densely Connected Convolutional Networks Gao Huang Zhuang Liu Laurens van der Maaten Kilian Q. Weinberger PINN 3DV 822 36,892 0 25 Aug 2016
Instance Normalization: The Missing Ingredient for Fast Stylization Dmitry Ulyanov Andrea Vedaldi Victor Lempitsky OOD 180 3,714 0 27 Jul 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.3K 194,510 0 10 Dec 2015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe Christian Szegedy OOD 467 43,347 0 11 Feb 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,129 0 20 Dec 2014
Going Deeper with Convolutions Christian Szegedy Wei Liu Yangqing Jia P. Sermanet Scott E. Reed Dragomir Anguelov D. Erhan Vincent Vanhoucke Andrew Rabinovich 494 43,717 0 17 Sep 2014
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 1.7K 39,615 0 01 Sep 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 289 14,968 1 21 Dec 2013