401. On Connections Between Regularizations for Improving DNN Robustness.
- Author
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Guo, Yiwen, Chen, Long, Chen, Yurong, and Zhang, Changshui
- Subjects
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MACHINE learning , *CURVATURE , *TASK analysis - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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