  Unsupervised domain adaptation (UDA) aims to predict unlabeled data from
target domain with access to labeled data from the source domain. In this work,
we propose a novel framework called SIDA (Surrogate Mutual Information
Maximization Domain Adaptation) with strong theoretical guarantees. To be
specific, SIDA implements adaptation by maximizing mutual information (MI)
between features. In the framework, a surrogate joint distribution models the
underlying joint distribution of the unlabeled target domain. Our theoretical
analysis validates SIDA by bounding the expected risk on target domain with MI
and surrogate distribution bias. Experiments show that our approach is
comparable with state-of-the-art unsupervised adaptation methods on standard
UDA tasks.
