Unlabeled Data Improves Adversarial Robustness.

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019)

Cited: 312|Views91
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Abstract

We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian model of Schmidt et al. [41] that shows a sample complexity gap between standard and robust classification. We prove that unlabeled data bridges this gap: a simple...More

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