Smoothness Analysis of Adversarial Training

2 March 2021

Papers citing "Smoothness Analysis of Adversarial Training"

29 / 29 papers shown

Title
Adversarial Training Makes Weight Loss Landscape Sharper in Logistic Regression Masanori Yamada Sekitoshi Kanai Tomoharu Iwata Tomokatsu Takahashi Yuki Yamanaka Hiroshi Takahashi Atsutoshi Kumagai AAML 106 9 0 05 Feb 2021
Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses Yihan Zhou V. S. Portella Mark Schmidt Nicholas J. A. Harvey 31 21 0 22 Oct 2020
Understanding Catastrophic Overfitting in Single-step Adversarial Training Hoki Kim Woojin Lee Jaewook Lee AAML 102 112 0 05 Oct 2020
Sharpness-Aware Minimization for Efficiently Improving Generalization Pierre Foret Ariel Kleiner H. Mobahi Behnam Neyshabur AAML 192 1,350 0 03 Oct 2020
S-SGD: Symmetrical Stochastic Gradient Descent with Weight Noise Injection for Reaching Flat Minima Wonyong Sung Iksoo Choi Jinhwan Park Seokhyun Choi Sungho Shin ODL 39 7 0 05 Sep 2020
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them Chen Liu Mathieu Salzmann Tao R. Lin Ryota Tomioka Sabine Süsstrunk AAML 103 82 0 15 Jun 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
Adversarial Robustness through Local Linearization Chongli Qin James Martens Sven Gowal Dilip Krishnan Krishnamurthy Dvijotham Alhussein Fawzi Soham De Robert Stanforth Pushmeet Kohli AAML 67 308 0 04 Jul 2019
Unlabeled Data Improves Adversarial Robustness Y. Carmon Aditi Raghunathan Ludwig Schmidt Percy Liang John C. Duchi 125 752 0 31 May 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang Tianyuan Zhang Yiping Lu Zhanxing Zhu Bin Dong AAML 107 361 0 02 May 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 152 2,039 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 134 2,551 0 24 Jan 2019
Evaluating and Understanding the Robustness of Adversarial Logit Pairing Logan Engstrom Andrew Ilyas Anish Athalye AAML 62 141 0 26 Jul 2018
Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks Yusuke Tsuzuku Issei Sato Masashi Sugiyama AAML 105 307 0 12 Feb 2018
Visualizing the Loss Landscape of Neural Nets Hao Li Zheng Xu Gavin Taylor Christoph Studer Tom Goldstein 243 1,893 0 28 Dec 2017
Improving Generalization Performance by Switching from Adam to SGD N. Keskar R. Socher ODL 98 524 0 20 Dec 2017
Three Factors Influencing Minima in SGD Stanislaw Jastrzebski Zachary Kenton Devansh Arpit Nicolas Ballas Asja Fischer Yoshua Bengio Amos Storkey 76 463 0 13 Nov 2017
Exploring Generalization in Deep Learning Behnam Neyshabur Srinadh Bhojanapalli David A. McAllester Nathan Srebro FAtt 150 1,256 0 27 Jun 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
Parseval Networks: Improving Robustness to Adversarial Examples Moustapha Cissé Piotr Bojanowski Edouard Grave Yann N. Dauphin Nicolas Usunier AAML 138 807 0 28 Apr 2017
Sharp Minima Can Generalize For Deep Nets Laurent Dinh Razvan Pascanu Samy Bengio Yoshua Bengio ODL 116 772 0 15 Mar 2017
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys Pratik Chaudhari A. Choromańska Stefano Soatto Yann LeCun Carlo Baldassi C. Borgs J. Chayes Levent Sagun R. Zecchina ODL 96 773 0 06 Nov 2016
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 472 3,144 0 04 Nov 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 424 2,941 0 15 Sep 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 340 7,985 0 23 May 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,020 0 10 Dec 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 102 3,072 0 14 Nov 2015
Train faster, generalize better: Stability of stochastic gradient descent Moritz Hardt Benjamin Recht Y. Singer 116 1,241 0 03 Sep 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 277 19,066 0 20 Dec 2014