Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks

25 September 2019

Tianyu Pang

Kun Xu

Jun Zhu

AAML

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Papers citing "Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks"

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Title
Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment Di Jin Zhijing Jin Qiufeng Wang Peter Szolovits SILM AAML 146 1,076 0 27 Jul 2019
Data Interpolating Prediction: Alternative Interpretation of Mixup Takuya Shimada Shoichiro Yamaguchi K. Hayashi Sosuke Kobayashi 62 7 0 20 Jun 2019
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Too Much Accuracy Alex Lamb Vikas Verma Kenji Kawaguchi Alexander Matyasko Savya Khosla Arno Solin Yoshua Bengio AAML 47 99 0 16 Jun 2019
MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot Nicholas Carlini Ian Goodfellow Nicolas Papernot Avital Oliver Colin Raffel 137 3,022 0 06 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 122 1,242 0 29 Apr 2019
Interpolation Consistency Training for Semi-Supervised Learning Vikas Verma Kenji Kawaguchi Alex Lamb Arno Solin Arno Solin Yoshua Bengio David Lopez-Paz 101 769 0 09 Mar 2019
On Adversarial Mixup Resynthesis Christopher Beckham S. Honari Vikas Verma Alex Lamb F. Ghadiri R. Devon Hjelm Yoshua Bengio C. Pal AAML 43 12 0 07 Mar 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 79 900 0 18 Feb 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 118 2,542 0 24 Jan 2019
MixUp as Locally Linear Out-Of-Manifold Regularization Hongyu Guo Yongyi Mao Richong Zhang 55 323 0 07 Sep 2018
Adversarial Attack on Graph Structured Data H. Dai Hui Li Tian Tian Xin Huang L. Wang Jun Zhu Le Song GNN AAML OOD 83 771 0 06 Jun 2018
Adversarial Attacks and Defences Competition Alexey Kurakin Ian Goodfellow Samy Bengio Yinpeng Dong Fangzhou Liao ... Junjiajia Long Yerkebulan Berdibekov Takuya Akiba Seiya Tokui Motoki Abe AAML SILM 86 320 0 31 Mar 2018
Max-Mahalanobis Linear Discriminant Analysis Networks Tianyu Pang Chao Du Jun Zhu 56 55 0 26 Feb 2018
Adversarial vulnerability for any classifier Alhussein Fawzi Hamza Fawzi Omar Fawzi AAML 70 249 0 23 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 183 3,180 0 01 Feb 2018
Data Augmentation by Pairing Samples for Images Classification H. Inoue 136 422 0 09 Jan 2018
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text Nicholas Carlini D. Wagner AAML 88 1,077 0 05 Jan 2018
Exploring the Landscape of Spatial Robustness Logan Engstrom Brandon Tran Dimitris Tsipras Ludwig Schmidt Aleksander Madry AAML 75 363 0 07 Dec 2017
Between-class Learning for Image Classification Yuji Tokozume Yoshitaka Ushiku Tatsuya Harada SSL 68 205 0 28 Nov 2017
Learning from Between-class Examples for Deep Sound Recognition Yuji Tokozume Yoshitaka Ushiku Tatsuya Harada SSL 72 237 0 28 Nov 2017
Mitigating Adversarial Effects Through Randomization Cihang Xie Jianyu Wang Zhishuai Zhang Zhou Ren Alan Yuille AAML 99 1,054 0 06 Nov 2017
Countering Adversarial Images using Input Transformations Chuan Guo Mayank Rana Moustapha Cissé Laurens van der Maaten AAML 102 1,400 0 31 Oct 2017
mixup: Beyond Empirical Risk Minimization Hongyi Zhang Moustapha Cissé Yann N. Dauphin David Lopez-Paz NoLa 269 9,743 0 25 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 263 12,029 0 19 Jun 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 118 1,854 0 20 May 2017
Adversarial Attacks on Neural Network Policies Sandy Huang Nicolas Papernot Ian Goodfellow Yan Duan Pieter Abbeel MLAU AAML 81 837 0 08 Feb 2017
Understanding deep learning requires rethinking generalization Chiyuan Zhang Samy Bengio Moritz Hardt Benjamin Recht Oriol Vinyals HAI 308 4,623 0 10 Nov 2016
Robustness of classifiers: from adversarial to random noise Alhussein Fawzi Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 57 374 0 31 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 517 5,885 0 08 Jul 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 324 10,172 0 16 Mar 2016
Exploring the Space of Adversarial Images Pedro Tabacof Eduardo Valle AAML 57 192 0 19 Oct 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 225 19,017 0 20 Dec 2014
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images Anh Totti Nguyen J. Yosinski Jeff Clune AAML 153 3,270 0 05 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 233 14,893 1 21 Dec 2013