Towards Evaluating the Robustness of Neural Networks Learned by Transduction

27 October 2021

Papers citing "Towards Evaluating the Robustness of Neural Networks Learned by Transduction"

31 / 31 papers shown

Title
Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Cost Cheng-Han Yeh Kuanchun Yu Chun-Shien Lu DiffM AAML 97 0 0 22 Oct 2024
On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning Yongyi Su Yushu Li Nanqing Liu Kui Jia Xulei Yang Chuan-Sheng Foo Xun Xu TTA AAML 91 1 0 07 Oct 2024
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks Dequan Wang An Ju Evan Shelhamer David Wagner Trevor Darrell AAML 102 27 0 18 May 2021
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 211 1,837 0 03 Mar 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 255 831 0 19 Feb 2020
Fast is better than free: Revisiting adversarial training Eric Wong Leslie Rice J. Zico Kolter AAML OOD 134 1,177 0 12 Jan 2020
An Adaptive and Momental Bound Method for Stochastic Learning Jianbang Ding Xuancheng Ren Ruixuan Luo Xu Sun ODL 37 46 0 27 Oct 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 121 751 0 31 May 2019
Benchmarking Neural Network Robustness to Common Corruptions and Perturbations Dan Hendrycks Thomas G. Dietterich OOD VLM 163 3,423 0 28 Mar 2019
A Research Agenda: Dynamic Models to Defend Against Correlated Attacks Ian Goodfellow AAML OOD 68 31 0 14 Mar 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 130 2,036 0 08 Feb 2019
Using Pre-Training Can Improve Model Robustness and Uncertainty Dan Hendrycks Kimin Lee Mantas Mazeika NoLa 69 726 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 129 2,542 0 24 Jan 2019
A Survey of Unsupervised Deep Domain Adaptation Garrett Wilson D. Cook OOD 80 814 0 06 Dec 2018
MMA Training: Direct Input Space Margin Maximization through Adversarial Training G. Ding Yash Sharma Kry Yik-Chau Lui Ruitong Huang AAML 56 272 0 06 Dec 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 250 3,191 0 20 Jun 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 131 789 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 199 3,180 0 01 Feb 2018
Certifying Some Distributional Robustness with Principled Adversarial Training Aman Sinha Hongseok Namkoong Riccardo Volpi John C. Duchi OOD 117 862 0 29 Oct 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 281 12,029 0 19 Jun 2017
The Space of Transferable Adversarial Examples Florian Tramèr Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML SILM 82 556 0 11 Apr 2017
Delving into Transferable Adversarial Examples and Black-box Attacks Yanpei Liu Xinyun Chen Chang-rui Liu D. Song AAML 133 1,731 0 08 Nov 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 241 8,548 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 517 5,893 0 08 Jul 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.1K 193,426 0 10 Dec 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 138 4,886 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 366 9,467 0 28 May 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 245 19,017 0 20 Dec 2014