  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 framework 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. Implementation is available at
https://github.com/VisionLearningGroup/DANCE.
