  Robust Unsupervised Domain Adaptation (RoUDA) aims to achieve not only clean
but also robust cross-domain knowledge transfer from a labeled source domain to
an unlabeled target domain. A number of works have been conducted by directly
injecting adversarial training (AT) in UDA based on the self-training pipeline
and then aiming to generate better adversarial examples (AEs) for AT. Despite
the remarkable progress, these methods only focus on finding stronger AEs but
neglect how to better learn from these AEs, thus leading to unsatisfied
results. In this paper, we investigate robust UDA from a representation
learning perspective and design a novel algorithm by utilizing the mutual
information theory, dubbed MIRoUDA. Specifically, through mutual information
optimization, MIRoUDA is designed to achieve three characteristics that are
highly expected in robust UDA, i.e., robustness, discrimination, and
generalization. We then propose a dual-model framework accordingly for robust
UDA learning. Extensive experiments on various benchmarks verify the
effectiveness of the proposed MIRoUDA, in which our method surpasses the
state-of-the-arts by a large margin.
