Unsupervised domain adaptation aims to learn a task classifier that performs well on the unlabeled target domain, by utilizing labeled source domain. Inspiring results have been acquired learning domain-invariant deep features via domain-adversarial training. However, its parallel design of and classifiers limits ability achieve finer category-level alignment. To promote categorical (CatDA), ba...