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Recent studies show that training deep neural networks (DNNs) with Lipschitz constraints are able to enhance adversarial robustness and other model properties such as stability.
Algorithms for the matrix pth root
Dario A Bini, Nicholas J Higham, and Beatrice Meini · 2005
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy · 2014
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Parseval networks: Improving robustness to adversarial examples
Moustapha Cisse, Piotr Bojanowski, Edouard Grave, Yann Dauphin, and Nicolas Usunier · 2017
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Improved bilinear pooling with cnns
Tsung-Yu Lin and Subhransu Maji · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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i-revnet: Deep invertible networks
Jörn-Henrik Jacobsen, Arnold Smeulders, and Edouard Oyallon · 2018
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Visualizing the loss landscape of neural nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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L2-nonexpansive neural networks
Haifeng Qian and Mark N Wegman · 2018
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The singular values of convolutional layers
Hanie Sedghi, Vineet Gupta, and Philip M Long · 2018
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Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks
Yusuke Tsuzuku, Issei Sato, and Masashi Sugiyama · 2018
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Characterizing adversarial examples based on spatial consistency information for semantic segmentation
Chaowei Xiao, Ruizhi Deng, Bo Li, Fisher Yu, Mingyan Liu, and Dawn Song · 2018
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Generating adversarial examples with adversarial networks
Chaowei Xiao, Bo Li, Jun-Yan Zhu, Warren He, Mingyan Liu, and Dawn Song · 2018
Cited alongside, same era.
Spatially transformed adversarial examples
Chaowei Xiao, Jun Yan Zhu, Bo Li, Warren He, Mingyan Liu, and Dawn Song · 2018
Cited alongside, same era.
Sorting out lipschitz function approximation
Cem Anil, James Lucas, and Roger Grosse · 2019
Cited alongside, same era.
Unlabeled data improves adversarial robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, John C Duchi, and Percy S Liang · 2019
Cited alongside, same era.
Efficient and accurate estimation of lipschitz constants for deep neural networks
Mahyar Fazlyab, Alexander Robey, Hamed Hassani, Manfred Morari, and George Pappas · 2019
Cited alongside, same era.
Preventing gradient attenuation in lipschitz constrained convolutional networks
Regularisation of neural networks by enforcing lipschitz continuity
Henry Gouk, Eibe Frank, Bernhard Pfahringer, and Michael J Cree · 2021
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Tss: Transformation-specific smoothing for robustness certification
Linyi Li, Maurice Weber, Xiaojun Xu, Luka Rimanic, Bhavya Kailkhura, Tao Xie, Ce Zhang, and Bo Li · 2021
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Skew orthogonal convolutions
Sahil Singla and Soheil Feizi · 2021
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Improved deterministic l2 robustness on cifar-10 and cifar-100
Sahil Singla, Surbhi Singla, and Soheil Feizi · 2021
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Orthogonalizing convolutional layers with the cayley transform
Asher Trockman and J Zico Kolter · 2021
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Trs: Transferability reduced ensemble via promoting gradient diversity and model smoothness
Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin I P Rubinstein, Ce Zhang, and Bo Li · 2021
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Qiyang Li, Saminul Haque, Cem Anil, James Lucas, Roger B Grosse, and Jörn-Henrik Jacobsen · 2019
Cited alongside, same era.
Advit: Adversarial frames identifier based on temporal consistency in videos
Chaowei Xiao, Ruizhi Deng, Bo Li, Taesung Lee, Benjamin Edwards, Jinfeng Yi, Dawn Song, Mingyan Liu, and Ian Molloy · 2019
Cited alongside, same era.
Controllable orthogonalization in training dnns
Lei Huang, Li Liu, Fan Zhu, Diwen Wan, Zehuan Yuan, Bo Li, and Ling Shao · 2020
Cited alongside, same era.
Lipschitz constant estimation of neural networks via sparse polynomial optimization
Fabian Latorre, Paul Rolland, and Volkan Cevher · 2020
Cited alongside, same era.
Semanticadv: Generating adversarial examples via attribute-conditioned image editing
Haonan Qiu, Chaowei Xiao, Lei Yang, Xinchen Yan, Honglak Lee, and Bo Li · 2020
Cited alongside, same era.
Orthogonal convolutional neural networks
Jiayun Wang, Yubei Chen, Rudrasis Chakraborty, and Stella X Yu · 2020
Cited alongside, same era.
Theoretical analysis of self-training with deep networks on unlabeled data
Colin Wei, Kendrick Shen, Yining Chen, and Tengyu Ma · 2020
Cited alongside, same era.
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Constructing orthogonal convolutions in an explicit manner
Tan Yu, Jun Li, Yunfeng Cai, and Ping Li · 2021
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Feature purification: How adversarial training performs robust deep learning
Zeyuan Allen-Zhu and Yuanzhi Li · 2022
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Double sampling randomized smoothing
Linyi Li, Jiawei Zhang, Tao Xie, and Bo Li · 2022
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On the certified robustness for ensemble models and beyond
Zhuolin Yang, Linyi Li, Xiaojun Xu, Bhavya Kailkhura, Tao Xie, and Bo Li · 2022
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Improving certified robustness via statistical learning with logical reasoning
Zhuolin Yang, Zhikuan Zhao, Boxin Wang, Jiawei Zhang, Linyi Li, Hengzhi Pei, Bojan Karlaš, Ji Liu, Heng Guo, Ce Zhang, and Bo Li · 2022
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Sok: Certified robustness for deep neural networks
Linyi Li, Tao Xie, and Bo Li · 2023
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