Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training

27 September 2022

Papers citing "Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training"

32 / 32 papers shown

Title
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 91 301 0 18 Oct 2021
SSD: A Unified Framework for Self-Supervised Outlier Detection Vikash Sehwag M. Chiang Prateek Mittal OODD 100 342 0 22 Mar 2021
Removing Undesirable Feature Contributions Using Out-of-Distribution Data Saehyung Lee Changhwa Park Hyungyu Lee Jihun Yi Jonghyun Lee Sungroh Yoon OODD 71 26 0 17 Jan 2021
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning Mamshad Nayeem Rizve Kevin Duarte Yogesh S Rawat M. Shah 311 519 0 15 Jan 2021
Bag of Tricks for Adversarial Training Tianyu Pang Xiao Yang Yinpeng Dong Hang Su Jun Zhu AAML 78 269 0 01 Oct 2020
What is being transferred in transfer learning? Behnam Neyshabur Hanie Sedghi Chiyuan Zhang 106 522 0 26 Aug 2020
Denoising Diffusion Probabilistic Models Jonathan Ho Ajay Jain Pieter Abbeel DiffM 645 18,096 0 19 Jun 2020
Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Saehyung Lee Hyungyu Lee Sungroh Yoon AAML 210 116 0 05 Mar 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 216 1,846 0 03 Mar 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 108 804 0 26 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations Ting-Li Chen Simon Kornblith Mohammad Norouzi Geoffrey E. Hinton SSL 369 18,778 0 13 Feb 2020
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence Kihyuk Sohn David Berthelot Chun-Liang Li Zizhao Zhang Nicholas Carlini E. D. Cubuk Alexey Kurakin Han Zhang Colin Raffel AAML 157 3,558 0 21 Jan 2020
What it Thinks is Important is Important: Robustness Transfers through Input Gradients Alvin Chan Yi Tay Yew-Soon Ong AAML OOD 53 52 0 11 Dec 2019
Square Attack: a query-efficient black-box adversarial attack via random search Maksym Andriushchenko Francesco Croce Nicolas Flammarion Matthias Hein AAML 85 988 0 29 Nov 2019
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack Francesco Croce Matthias Hein AAML 93 488 0 03 Jul 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 127 752 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 74 334 0 31 May 2019
Cross-Domain Transferability of Adversarial Perturbations Muzammal Naseer Salman H. Khan M. H. Khan Fahad Shahbaz Khan Fatih Porikli AAML 91 145 0 28 May 2019
Adversarially robust transfer learning Ali Shafahi Parsa Saadatpanah Chen Zhu Amin Ghiasi Christoph Studer David Jacobs Tom Goldstein OOD 44 116 0 20 May 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 91 1,838 0 06 May 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 73 725 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 137 2,551 0 24 Jan 2019
Deep Anomaly Detection with Outlier Exposure Dan Hendrycks Mantas Mazeika Thomas G. Dietterich OODD 183 1,483 0 11 Dec 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 104 1,781 0 30 May 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 146 790 0 30 Apr 2018
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey Naveed Akhtar Ajmal Mian AAML 97 1,867 0 02 Jan 2018
A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets P. Chrabaszcz I. Loshchilov Frank Hutter SSeg OOD 161 647 0 27 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 307 12,069 0 19 Jun 2017
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 266 8,555 0 16 Aug 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 349 7,985 0 23 May 2016
Learning Multi-Domain Convolutional Neural Networks for Visual Tracking Hyeonseob Nam Bohyung Han 101 2,475 0 27 Oct 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 19,066 0 20 Dec 2014