  Unsupervised domain adaptation addresses the problem of classifying data in
an unlabeled target domain, given labeled source domain data that share a
common label space but follow a different distribution. Most of the recent
methods take the approach of explicitly aligning feature distributions between
the two domains. Differently, motivated by the fundamental assumption for
domain adaptability, we re-cast the domain adaptation problem as discriminative
clustering of target data, given strong privileged information provided by the
closely related, labeled source data. Technically, we use clustering objectives
based on a robust variant of entropy minimization that adaptively filters
target data, a soft Fisher-like criterion, and additionally the cluster
ordering via centroid classification. To distill discriminative source
information for target clustering, we propose to jointly train the network
using parallel, supervised learning objectives over labeled source data. We
term our method of distilled discriminative clustering for domain adaptation as
DisClusterDA. We also give geometric intuition that illustrates how constituent
objectives of DisClusterDA help learn class-wisely pure, compact feature
distributions. We conduct careful ablation studies and extensive experiments on
five popular benchmark datasets, including a multi-source domain adaptation
one. Based on commonly used backbone networks, DisClusterDA outperforms
existing methods on these benchmarks. It is also interesting to observe that in
our DisClusterDA framework, adding an additional loss term that explicitly
learns to align class-level feature distributions across domains does harm to
the adaptation performance, though more careful studies in different
algorithmic frameworks are to be conducted.
