Enhancing Adversarial Robustness of Deep Neural Networks

  • Author : Jeffrey Zhang (M. Eng.)
  • Publsiher : Anonim
  • Release : 20 October 2021
  • ISBN : OCLC:1127291827
  • Page : 58 pages
  • Rating : 4/5 from 21 voters

Download or read online book entitled Enhancing Adversarial Robustness of Deep Neural Networks written by Jeffrey Zhang (M. Eng.) and published by Anonim. This book was released on 20 October 2021 with total page 58 pages. Available in PDF, EPUB and Kindle. Get best books that you want by click Get Book Button and Read as many books as you like. Book Excerpt : Logit-based regularization and pretrain-then-tune are two approaches that have recently been shown to enhance adversarial robustness of machine learning models. In the realm of regularization, Zhang et al. (2019) proposed TRADES, a logit-based regularization optimization function that has been shown to improve upon the robust optimization framework developed by Madry et al. (2018) [14, 9]. They were able to achieve state-of-the-art adversarial accuracy on CIFAR10. In the realm of pretrain- then-tune models, Hendrycks el al. (2019) demonstrated that adversarially pretraining a model on ImageNet then adversarially tuning on CIFAR10 greatly improves the adversarial robustness of machine learning models. In this work, we propose Adversarial Regularization, another logit-based regularization optimization framework that surpasses TRADES in adversarial generalization. Furthermore, we explore the impact of trying different types of adversarial training on the pretrain-then-tune paradigm.

Enhancing Adversarial Robustness of Deep Neural Networks

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Author: Jeffrey Zhang (M. Eng.)
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Relase: 2019
ISBN: OCLC:1127291827
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