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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2020.3006917
|2 doi
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|a eng
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| 100 |
1 |
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|a Guo, Yiwen
|e verfasserin
|4 aut
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| 245 |
1 |
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|a On Connections Between Regularizations for Improving DNN Robustness
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|c 2021
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| 336 |
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|a Text
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|a Date Completed 10.12.2021
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|a Date Revised 14.12.2021
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a This paper analyzes regularization terms proposed recently for improving the adversarial robustness of deep neural networks (DNNs), from a theoretical point of view. Specifically, we study possible connections between several effective methods, including input-gradient regularization, Jacobian regularization, curvature regularization, and a cross-Lipschitz functional. We investigate them on DNNs with general rectified linear activations, which constitute one of the most prevalent families of models for image classification and a host of other machine learning applications. We shed light on essential ingredients of these regularizations and re-interpret their functionality. Through the lens of our study, more principled and efficient regularizations can possibly be invented in the near future
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|a Journal Article
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|a Chen, Long
|e verfasserin
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| 700 |
1 |
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|a Chen, Yurong
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zhang, Changshui
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 43(2021), 12 vom: 30. Dez., Seite 4469-4476
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|u http://dx.doi.org/10.1109/TPAMI.2020.3006917
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