Unsupervised domain adaptation has received significant attention in recent
years. Most of existing works tackle the closed-set scenario, assuming that the
source and target domains share the exactly same categories. In practice,
nevertheless, a target domain often contains samples of classes unseen in
source domain (i.e., unknown class). The extension of domain adaptation from
closed-set to such open-set situation is not trivial since the target samples
in unknown class are not expected to align with the source. In this paper, we
address this problem by augmenting the state-of-the-art domain adaptation
technique, Self-Ensembling, with category-agnostic clusters in target domain.
Specifically, we present Self-Ensembling with Category-agnostic Clusters
(SE-CC) -- a novel architecture that steers domain adaptation with the
additional guidance of category-agnostic clusters that are specific to target
domain. These clustering information provides domain-specific visual cues,
facilitating the generalization of Self-Ensembling for both closed-set and
open-set scenarios. Technically, clustering is firstly performed over all the
unlabeled target samples to obtain the category-agnostic clusters, which reveal
the underlying data space structure peculiar to target domain. A clustering
branch is capitalized on to ensure that the learnt representation preserves
such underlying structure by matching the estimated assignment distribution
over clusters to the inherent cluster distribution for each target sample.
Furthermore, SE-CC enhances the learnt representation with mutual information
maximization. Extensive experiments are conducted on Office and VisDA datasets
for both open-set and closed-set domain adaptation, and superior results are
reported when comparing to the state-of-the-art approaches.

本文提出了一种用于处理源领域和目标领域中可能存在未知类别的自己集成（Self-Ensembling）方法，称为具有类别不可知聚类（Category-agnostic Clusters）的 SE-CC 模型，该模型利用聚类信息提供了与目标领域特定的视觉提示，方便了对于开集和闭集领域自适应的泛化。