Unsupervised domain adaptation methods traditionally assume that all source categories are present in the target domain. In practice, little may be known about the category overlap between the two domains. While some methods address target settings with either partial or open-set categories, they assume that the particular setting is known a priori. We propose a more universally applicable domain adaptation approach that can handle arbitrary category shift, called Domain Adaptative Neighborhood Clustering via Entropy optimization (DANCE). DANCE combines two novel ideas: First, as we cannot fully rely on source categories to learn features discriminative for the target, we propose a novel neighborhood clustering technique to learn the structure of the target domain in a self-supervised way. Second, we use entropy-based feature alignment and rejection to align target features with the source, or reject them as unknown categories based on their entropy. We show through extensive experiments that DANCE outperforms baselines across open-set, open-partial and partial domain adaptation settings.

该论文提出了一种更具普适性的领域自适应框架，称为通过熵优化的领域自适应邻域聚类（DANCE），可以处理任意类别转移。DANCE结合两个新颖的想法：第一，我们提出了一种新的邻域聚类技术，在无监督的情况下学习目标域的结构。第二，我们利用基于熵的特征对齐和拒绝来对齐目标特征与源特征，或根据其熵拒绝它们作为未知类别。实验结果表明，DANCE在开放集，开放部分和部分领域自适应设置中优于基线。

自监督的通用领域适应