Towards Adversarial Robustness via Transductive Learning

15 June 2021

Papers citing "Towards Adversarial Robustness via Transductive Learning"

24 / 24 papers shown

Title
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples S. Goldwasser Adam Tauman Kalai Y. Kalai Omar Montasser AAML 45 40 0 10 Jul 2020
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations Ching-Yao Chuang Antonio Torralba Stefanie Jegelka OOD 29 62 0 06 Jul 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
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 272 833 0 19 Feb 2020
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 121 752 0 31 May 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks Thomas G. Dietterich OOD VLM 170 3,431 0 28 Mar 2019
A Research Agenda: Dynamic Models to Defend Against Correlated Attacks Ian Goodfellow AAML OOD 71 31 0 14 Mar 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 141 2,038 0 08 Feb 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 129 2,549 0 24 Jan 2019
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 267 3,195 0 20 Jun 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 131 790 0 30 Apr 2018
Stochastic Hyperparameter Optimization through Hypernetworks Jonathan Lorraine David Duvenaud 71 140 0 26 Feb 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 216 3,185 0 01 Feb 2018
Certifying Some Distributional Robustness with Principled Adversarial Training Aman Sinha Hongseok Namkoong Riccardo Volpi John C. Duchi OOD 125 863 0 29 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 304 12,063 0 19 Jun 2017
The Space of Transferable Adversarial Examples Florian Tramèr Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML SILM 82 558 0 11 Apr 2017
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu D. Song AAML 140 1,737 0 08 Nov 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 258 8,550 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 540 5,897 0 08 Jul 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 193,878 0 10 Dec 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 148 4,895 0 14 Nov 2015
Domain-Adversarial Training of Neural Networks Yaroslav Ganin E. Ustinova Hana Ajakan Pascal Germain Hugo Larochelle François Laviolette M. Marchand Victor Lempitsky GAN OOD 369 9,486 0 28 May 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 271 19,049 0 20 Dec 2014
Domain-Adversarial Neural Networks Hana Ajakan Pascal Germain Hugo Larochelle François Laviolette M. Marchand OOD GAN DRL 87 309 0 15 Dec 2014