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Adversarial robustness has received increasing attention along with the study of adversarial examples.
On the momentum term in gradient descent learning algorithms
Ning Qian · 1999
Earlier work this paper cites.
An analysis of single-layer networks in unsupervised feature learning
Adam Coates, Andrew Ng, and Honglak Lee · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Fei-Fei Li · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
Earlier work this paper cites.
Defensive distillation is not robust to adversarial examples
Nicholas Carlini and David Wagner · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2016
Earlier work this paper cites.
Parseval networks: Improving robustness to adversarial examples
Moustapha Cissé, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
Earlier work this paper cites.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Earlier work this paper cites.
Residual convolutional ctc networks for automatic speech recognition
Yisen Wang, Xuejiao Deng, Songbai Pu, and Zhiheng Huang · 2017
Earlier work this paper cites.
Can we gain more from orthogonality regularizations in training deep networks?
Nitin Bansal, Xiaohan Chen, and Zhangyang Wang · 2018
Earlier work this paper cites.
Compression to the rescue: Defending from adversarial attacks across modalities
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen, Fred Hohman, Siwei Li, Li Chen, Michael E Kounavis, and Duen Horng Chau · 2018
Cited alongside, same era.
Improving dnn robustness to adversarial attacks using jacobian regularization
Daniel Jakubovitz and Raja Giryes · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
Deep defense: Training dnns with improved adversarial robustness
Ziang Yan, Yiwen Guo, and Changshui Zhang · 2018
Cited alongside, same era.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2019
Cited alongside, same era.
Improving query efficiency of black-box adversarial attack
Yang Bai, Yuyuan Zeng, Yong Jiang, Yisen Wang, Shu-Tao Xia, and Weiwei Guo · 2020
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
Francesco Croce and Matthias Hein · 2020
Later among the works it cites.
Backpropagating linearly improves transferability of adversarial examples
Yiwen Guo, Qizhang Li, and Hao Chen · 2020
Later among the works it cites.
Ziquan Liu, Yufei Cui, and Antoni B Chan · 2020
Later among the works it cites.
Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Batch normalization is a cause of adversarial vulnerability
Angus Galloway, Anna Golubeva, Thomas Tanay, Medhat Moussa, and Graham W Taylor · 2019
Cited alongside, same era.
Comdefend: An efficient image compression model to defend adversarial examples
Xiaojun Jia, Xingxing Wei, Xiaochun Cao, and Hassan. Foroosh · 2019
Cited alongside, same era.
Nattack: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks
Yandong Li, Lijun Li, Liqiang Wang, Tong Zhang, and Boqing Gong · 2019
Cited alongside, same era.
Adversarial training generalizes data-dependent spectral norm regularization
Kevin Roth, Yannic Kilcher, and Thomas Hofmann · 2019
Cited alongside, same era.
On the convergence and robustness of adversarial training
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu · 2019
Cited alongside, same era.
Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 2019
Cited alongside, same era.
Breeds: Benchmarks for subpopulation shift
Shibani Santurkar, Dimitris Tsipras, and Aleksander Madry · 2020
Later among the works it cites.
Improving adversarial robustness requires revisiting misclassified examples
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu · 2020
Later among the works it cites.
Skip connections matter: On the transferability of adversarial examples generated with resnets
Dongxian Wu, Yisen Wang, Shu-Tao Xia, James Bailey, and Xingjun Ma · 2020
Later among the works it cites.
Adversarial weight perturbation helps robust generalization
Dongxian Wu, Shu-Tao Xia, and Yisen Wang · 2020
Later among the works it cites.
Adversarial defense via local flatness regularization
Jia Xu, Yiming Li, Yong Jiang, and Shu-Tao Xia · 2020
Later among the works it cites.
Improving adversarial robustness via channel-wise activation suppressing
Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, and Yisen Wang · 2021
Closest in time.
Analysis and applications of class-wise robustness in adversarial training
Qi Tian, Kun Kuang, Kelu Jiang, Fei Wu, and Yisen Wang · 2021
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