Overfitting in adversarially robust deep learning

26 February 2020

Papers citing "Overfitting in adversarially robust deep learning"

32 / 182 papers shown

Title
Impact of Spatial Frequency Based Constraints on Adversarial Robustness Rémi Bernhard Pierre-Alain Moëllic Martial Mermillod Yannick Bourrier Romain Cohendet M. Solinas M. Reyboz AAML 24 17 0 26 Apr 2021
Random Noise Defense Against Query-Based Black-Box Attacks Zeyu Qin Yanbo Fan H. Zha Baoyuan Wu AAML 19 59 0 23 Apr 2021
LAFEAT: Piercing Through Adversarial Defenses with Latent Features Yunrui Yu Xitong Gao Chengzhong Xu AAML FedML 33 44 0 19 Apr 2021
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 32 65 0 09 Apr 2021
Adversarial Robustness under Long-Tailed Distribution Tong Wu Ziwei Liu Qingqiu Huang Yu Wang Dahua Lin 21 76 0 06 Apr 2021
Adversarially Optimized Mixup for Robust Classification Jason Bunk Srinjoy Chattopadhyay B. S. Manjunath S. Chandrasekaran AAML 30 8 0 22 Mar 2021
Consistency Regularization for Adversarial Robustness Jihoon Tack Sihyun Yu Jongheon Jeong Minseon Kim Sung Ju Hwang Jinwoo Shin AAML 41 57 0 08 Mar 2021
Dynamic Efficient Adversarial Training Guided by Gradient Magnitude Fu Lee Wang Yanghao Zhang Yanbin Zheng Wenjie Ruan 23 1 0 04 Mar 2021
Fixing Data Augmentation to Improve Adversarial Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 36 269 0 02 Mar 2021
Low Curvature Activations Reduce Overfitting in Adversarial Training Vasu Singla Sahil Singla David Jacobs S. Feizi AAML 32 45 0 15 Feb 2021
Guided Interpolation for Adversarial Training Chen Chen Jingfeng Zhang Xilie Xu Tianlei Hu Gang Niu Gang Chen Masashi Sugiyama AAML 30 10 0 15 Feb 2021
Mixed Nash Equilibria in the Adversarial Examples Game Laurent Meunier M. Scetbon Rafael Pinot Jamal Atif Y. Chevaleyre AAML 20 29 0 13 Feb 2021
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 16 9 0 05 Feb 2021
Context-Aware Safe Reinforcement Learning for Non-Stationary Environments Baiming Chen Zuxin Liu Jiacheng Zhu Mengdi Xu Wenhao Ding Ding Zhao 25 35 0 02 Jan 2021
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses Gaurang Sriramanan Sravanti Addepalli Arya Baburaj R. Venkatesh Babu AAML 22 92 0 30 Nov 2020
Learnable Boundary Guided Adversarial Training Jiequan Cui Shu-Lin Liu Liwei Wang Jiaya Jia OOD AAML 24 124 0 23 Nov 2020
Recent Advances in Understanding Adversarial Robustness of Deep Neural Networks Tao Bai Jinqi Luo Jun Zhao AAML 49 8 0 03 Nov 2020
Robust Pre-Training by Adversarial Contrastive Learning Ziyu Jiang Tianlong Chen Ting-Li Chen Zhangyang Wang 30 226 0 26 Oct 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 29 48 0 19 Oct 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 17 323 0 07 Oct 2020
Geometry-aware Instance-reweighted Adversarial Training Jingfeng Zhang Jianing Zhu Gang Niu Bo Han Masashi Sugiyama Mohan S. Kankanhalli AAML 47 269 0 05 Oct 2020
Adversarial Training with Stochastic Weight Average Joong-won Hwang Youngwan Lee Sungchan Oh Yuseok Bae OOD AAML 21 11 0 21 Sep 2020
Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman Andrew Ilyas Logan Engstrom Ashish Kapoor A. Madry 37 416 0 16 Jul 2020
Boundary thickness and robustness in learning models Yaoqing Yang Rekha Khanna Yaodong Yu A. Gholami Kurt Keutzer Joseph E. Gonzalez Kannan Ramchandran Michael W. Mahoney OOD 18 37 0 09 Jul 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 24 81 0 15 Jun 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 26 18 0 19 May 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 75 98 0 20 Mar 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang Xilie Xu Bo Han Gang Niu Li-zhen Cui Masashi Sugiyama Mohan S. Kankanhalli AAML 27 396 0 26 Feb 2020
Adversarial Attack Generation Empowered by Min-Max Optimization Jingkang Wang Tianyun Zhang Sijia Liu Pin-Yu Chen Jiacen Xu M. Fardad Yangqiu Song AAML 25 35 0 09 Jun 2019
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 240 1,837 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang M. Kwiatkowska Sen Wang Min Wu AAML 180 932 0 21 Oct 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,837 0 08 Jul 2016