  Deep learning methods have shown promise in unsupervised domain adaptation,
which aims to leverage a labeled source domain to learn a classifier for the
unlabeled target domain with a different distribution. However, such methods
typically learn a domain-invariant representation space to match the marginal
distributions of the source and target domains, while ignoring their fine-level
structures. In this paper, we propose Cluster Alignment with a Teacher (CAT)
for unsupervised domain adaptation, which can effectively incorporate the
discriminative clustering structures in both domains for better adaptation.
Technically, CAT leverages an implicit ensembling teacher model to reliably
discover the class-conditional structure in the feature space for the unlabeled
target domain. Then CAT forces the features of both the source and the target
domains to form discriminative class-conditional clusters and aligns the
corresponding clusters across domains. Empirical results demonstrate that CAT
achieves state-of-the-art results in several unsupervised domain adaptation
scenarios.
