Unlabeled Data Improves Adversarial Robustness.
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 32 (NIPS 2019), (2019)
EI
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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