  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. that shows a sample
complexity gap between standard and robust classification. We prove that
unlabeled data bridges this gap: a simple semisupervised learning procedure
(self-training) achieves high robust accuracy using the same number of labels
required for achieving high standard accuracy. Empirically, we augment CIFAR-10
with 500K unlabeled images sourced from 80 Million Tiny Images and use robust
self-training to outperform state-of-the-art robust accuracies by over 5 points
in (i) $\ell_\infty$ robustness against several strong attacks via adversarial
training and (ii) certified $\ell_2$ and $\ell_\infty$ robustness via
randomized smoothing. On SVHN, adding the dataset's own extra training set with
the labels removed provides gains of 4 to 10 points, within 1 point of the gain
from using the extra labels.
