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Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization

3 February 2022

Papers citing "Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization"

34 / 34 papers shown

Title
Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization Yize Zhang Yingzhe Xu Junyu Shi L. Zhang Shengshan Hu Minghui Li Yanjun Zhang AAML 137 2 0 17 Mar 2025
f-Domain-Adversarial Learning: Theory and Algorithms David Acuna Guojun Zhang M. Law Sanja Fidler FedML AI4CE 68 62 0 21 Jun 2021
Adversarial Training Helps Transfer Learning via Better Representations Zhun Deng Linjun Zhang Kailas Vodrahalli Kenji Kawaguchi James Zou GAN 87 54 0 18 Jun 2021
k-Mixup Regularization for Deep Learning via Optimal Transport Kristjan Greenewald Anming Gu Mikhail Yurochkin Justin Solomon Edward Chien 82 19 0 05 Jun 2021
TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness Zhuolin Yang Linyi Li Xiaojun Xu Shiliang Zuo Qiang Chen Benjamin I. P. Rubinstein Pan Zhou Ce Zhang Yue Liu AAML 118 55 0 01 Apr 2021
An Online Learning Approach to Interpolation and Extrapolation in Domain Generalization Elan Rosenfeld Pradeep Ravikumar Andrej Risteski 100 36 0 25 Feb 2021
How Does Mixup Help With Robustness and Generalization? Linjun Zhang Zhun Deng Kenji Kawaguchi Amirata Ghorbani James Zou AAML 94 252 0 09 Oct 2020
Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman Andrew Ilyas Logan Engstrom Ashish Kapoor Aleksander Madry 93 426 0 16 Jul 2020
Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability Kaizhao Liang Jacky Y. Zhang Wei Ping Zhuolin Yang Oluwasanmi Koyejo Yangqiu Song AAML 106 26 0 25 Jun 2020
Controllable Orthogonalization in Training DNNs Lei Huang Li Liu Fan Zhu Diwen Wan Zehuan Yuan Bo Li Ling Shao 75 44 0 02 Apr 2020
TSS: Transformation-Specific Smoothing for Robustness Certification Linyi Li Maurice Weber Xiaojun Xu Luka Rimanic B. Kailkhura Tao Xie Ce Zhang Yue Liu AAML 100 57 0 27 Feb 2020
Robust Learning with Jacobian Regularization Judy Hoffman Daniel A. Roberts Sho Yaida OOD AAML 64 169 0 07 Aug 2019
Further advantages of data augmentation on convolutional neural networks Alex Hernández-García Peter König 59 108 0 26 Jun 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 95 1,845 0 06 May 2019
HopSkipJumpAttack: A Query-Efficient Decision-Based Attack Jianbo Chen Michael I. Jordan Martin J. Wainwright AAML 104 670 0 03 Apr 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 169 2,052 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 159 2,560 0 24 Jan 2019
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation Chaowei Xiao Ruizhi Deng Yue Liu Feng Yu M. Liu Basel Alomair AAML 50 99 0 11 Oct 2018
Learning Invariances using the Marginal Likelihood Mark van der Wilk Matthias Bauer S. T. John J. Hensman 84 86 0 16 Aug 2018
Data augmentation instead of explicit regularization Alex Hernández-García Peter König 76 144 0 11 Jun 2018
AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks Chun-Chen Tu Pai-Shun Ting Pin-Yu Chen Sijia Liu Huan Zhang Jinfeng Yi Cho-Jui Hsieh Shin-Ming Cheng MLAU AAML 84 398 0 30 May 2018
A Kernel Theory of Modern Data Augmentation Tri Dao Albert Gu Alexander J. Ratner Virginia Smith Christopher De Sa Christopher Ré 110 193 0 16 Mar 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma Yue Liu Yisen Wang S. Erfani S. Wijewickrema Grant Schoenebeck Basel Alomair Michael E. Houle James Bailey AAML 114 742 0 08 Jan 2018
The Effectiveness of Data Augmentation in Image Classification using Deep Learning Luis Perez Jason Wang 84 2,793 0 13 Dec 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 319 12,138 0 19 Jun 2017
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Takeru Miyato S. Maeda Masanori Koyama S. Ishii GAN 153 2,739 0 13 Apr 2017
Dataset Augmentation in Feature Space Terrance Devries Graham W. Taylor 78 427 0 17 Feb 2017
Understanding data augmentation for classification: when to warp? S. Wong Adam Gatt V. Stamatescu Mark D Mcdonnell 80 906 0 28 Sep 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 282 8,587 0 16 Aug 2016
Optimal Transport for Domain Adaptation Nicolas Courty Rémi Flamary D. Tuia A. Rakotomamonjy OT OOD 155 1,125 0 02 Jul 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 282 19,129 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 289 14,968 1 21 Dec 2013
Domain Generalization via Invariant Feature Representation Krikamol Muandet David Balduzzi Bernhard Schölkopf OOD 144 1,188 0 10 Jan 2013
Domain Adaptation: Learning Bounds and Algorithms Yishay Mansour M. Mohri Afshin Rostamizadeh 303 801 0 19 Feb 2009