2021-03-18-deng21b.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Improving Adversarial Robustness via Unlabeled Out-of-Domain Data	Data augmentation by incorporating cheap unlabeled data from multiple domains is a powerful way to improve prediction especially when there is limited labeled data. In this work, we investigate how adversarial robustness can be enhanced by leveraging out-of-domain unlabeled data. We demonstrate that for broad classes of distributions and classifiers, there exists a sample complexity gap between standard and robust classification. We quantify the extent to which this gap can be bridged by leveraging unlabeled samples from a shifted domain by providing both upper and lower bounds. Moreover, we show settings where we achieve better adversarial robustness when the unlabeled data come from a shifted domain rather than the same domain as the labeled data. We also investigate how to leverage out-of-domain data when some structural information, such as sparsity, is shared between labeled and unlabeled domains. Experimentally, we augment object recognition datasets (CIFAR-10, CINIC-10, and SVHN) with easy-to-obtain and unlabeled out-of-domain data and demonstrate substantial improvement in the model’s robustness against $\ell_\infty$ adversarial attacks on the original domain.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	deng21b	0	Improving Adversarial Robustness via Unlabeled Out-of-Domain Data	2845	2853	2845-2853	2845	false	Deng, Zhun and Zhang, Linjun and Ghorbani, Amirata and Zou, James	given family Zhun Deng given family Linjun Zhang given family Amirata Ghorbani given family James Zou	2021-03-18		Proceedings of The 24th International Conference on Artificial Intelligence and Statistics	130	inproceedings	date-parts 2021 3 18	http://proceedings.mlr.press/v130/deng21b/deng21b.pdf	label link Supplementary PDF http://proceedings.mlr.press/v130/deng21b/deng21b-supp.pdf