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.

该研究提出了一种名为 DisClusterDA 的新方法，将无监督领域适应问题重新表述为目标数据的判别式聚类，使用基于熵最小化、软 Fisher 准则和质心分类的聚类目标自适应地过滤目标数据来蒸馏判别式源信息，同时结合并行的受监督学习目标来训练网络，经过对 5 个基准数据集进行的实验表明，DisClusterDA 在这些数据集上的表现优于现有的方法。