Adversarial Interpolation Training: A Simple Approach for Improving Model RobustnessDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • Keywords: adversarial training, adversarial robustness
  • TL;DR: adversarial interpolation training: a simple, intuitive and effective approach for improving model robustness
  • Abstract: We propose a simple approach for adversarial training. The proposed approach utilizes an adversarial interpolation scheme for generating adversarial images and accompanying adversarial labels, which are then used in place of the original data for model training. The proposed approach is intuitive to understand, simple to implement and achieves state-of-the-art performance. We evaluate the proposed approach on a number of datasets including CIFAR10, CIFAR100 and SVHN. Extensive empirical results compared with several state-of-the-art methods against different attacks verify the effectiveness of the proposed approach.
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