Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unlabeled Data Improves Adversarial Robustness   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.

23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol