v1v2v3v4 (latest)

Are Labels Required for Improving Adversarial Robustness?

31 May 2019

J. Uesato

Jean-Baptiste Alayrac

Papers citing "Are Labels Required for Improving Adversarial Robustness?"

43 / 43 papers shown

Title
TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification Dongyoon Yang Jihu Lee Yongdai Kim 85 0 0 10 May 2025
Bias-Augmented Consistency Training Reduces Biased Reasoning in Chain-of-Thought James Chua Edward Rees Hunar Batra Samuel R. Bowman Julian Michael Ethan Perez Miles Turpin LRM 116 13 0 08 Mar 2024
Generating Less Certain Adversarial Examples Improves Robust Generalization Minxing Zhang Michael Backes Xiao Zhang AAML 110 1 0 06 Oct 2023
An Alternative Surrogate Loss for PGD-based Adversarial Testing Sven Gowal J. Uesato Chongli Qin Po-Sen Huang Timothy A. Mann Pushmeet Kohli AAML 99 89 0 21 Oct 2019
Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai Tianle Cai Di He Chen Dan Kun He John E. Hopcroft Liwei Wang 68 158 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
Robustness to Adversarial Perturbations in Learning from Incomplete Data Amir Najafi S. Maeda Masanori Koyama Takeru Miyato OOD 83 131 0 24 May 2019
Semi-Supervised Learning with Scarce Annotations Sylvestre-Alvise Rebuffi Sébastien Ehrhardt Kai Han Andrea Vedaldi Andrew Zisserman SSL 54 51 0 21 May 2019
MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot Nicholas Carlini Ian Goodfellow Nicolas Papernot Avital Oliver Colin Raffel 151 3,033 0 06 May 2019
Unsupervised Data Augmentation for Consistency Training Qizhe Xie Zihang Dai Eduard H. Hovy Minh-Thang Luong Quoc V. Le 135 2,320 0 29 Apr 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 76 727 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 140 2,559 0 24 Jan 2019
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models Sven Gowal Krishnamurthy Dvijotham Robert Stanforth Rudy Bunel Chongli Qin J. Uesato Relja Arandjelović Timothy A. Mann Pushmeet Kohli AAML 82 558 0 30 Oct 2018
Rademacher Complexity for Adversarially Robust Generalization Dong Yin Kannan Ramchandran Peter L. Bartlett AAML 92 261 0 29 Oct 2018
Adversarial Risk Bounds via Function Transformation Justin Khim Po-Ling Loh AAML 66 50 0 22 Oct 2018
Scaling provable adversarial defenses Eric Wong Frank R. Schmidt J. H. Metzen J. Zico Kolter AAML 78 450 0 31 May 2018
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei Maya Kabkab Rama Chellappa AAML GAN 86 1,178 0 17 May 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 152 794 0 30 Apr 2018
Group Normalization Yuxin Wu Kaiming He 233 3,669 0 22 Mar 2018
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks J. Uesato Brendan O'Donoghue Aaron van den Oord Pushmeet Kohli AAML 160 606 0 15 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 243 3,194 0 01 Feb 2018
PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples Yang Song Taesup Kim Sebastian Nowozin Stefano Ermon Nate Kushman AAML 110 791 0 30 Oct 2017
Evasion Attacks against Machine Learning at Test Time Battista Biggio Igino Corona Davide Maiorca B. Nelson Nedim Srndic Pavel Laskov Giorgio Giacinto Fabio Roli AAML 157 2,159 0 21 Aug 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 310 12,117 0 19 Jun 2017
Deep Learning is Robust to Massive Label Noise David Rolnick Andreas Veit Serge J. Belongie Nir Shavit NoLa 88 557 0 30 May 2017
Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods Nicholas Carlini D. Wagner AAML 131 1,864 0 20 May 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,728 0 19 May 2017
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Takeru Miyato S. Maeda Masanori Koyama S. Ishii GAN 148 2,738 0 13 Apr 2017
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu Basel Alomair AAML 140 1,741 0 08 Nov 2016
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 472 3,147 0 04 Nov 2016
Temporal Ensembling for Semi-Supervised Learning S. Laine Timo Aila UQCV 185 2,566 0 07 Oct 2016
Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach Giorgio Patrini A. Rozza A. Menon Richard Nock Zhuang Li NoLa 110 1,458 0 13 Sep 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 266 8,579 0 16 Aug 2016
Instance Normalization: The Missing Ingredient for Fast Stylization Dmitry Ulyanov Andrea Vedaldi Victor Lempitsky OOD 177 3,711 0 27 Jul 2016
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning Mehdi S. M. Sajjadi Mehran Javanmardi Tolga Tasdizen BDL 85 1,115 0 14 Jun 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 351 7,995 0 23 May 2016
End to End Learning for Self-Driving Cars Mariusz Bojarski D. Testa Daniel Dworakowski Bernhard Firner B. Flepp ... Urs Muller Jiakai Zhang Xin Zhang Jake Zhao Karol Zieba SSL 100 4,175 0 25 Apr 2016
Improving the Robustness of Deep Neural Networks via Stability Training Stephan Zheng Yang Song Thomas Leung Ian Goodfellow OOD 50 639 0 15 Apr 2016
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.0K 150,260 0 22 Dec 2014
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 280 19,107 0 20 Dec 2014
Learning with Pseudo-Ensembles Philip Bachman O. Alsharif Doina Precup 89 601 0 16 Dec 2014
Towards Deep Neural Network Architectures Robust to Adversarial Examples S. Gu Luca Rigazio AAML 76 844 0 11 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 280 14,961 1 21 Dec 2013