  Active domain adaptation (ADA) aims to improve the model adaptation
performance by incorporating active learning (AL) techniques to label a
maximally-informative subset of target samples. Conventional AL methods do not
consider the existence of domain shift, and hence, fail to identify the truly
valuable samples in the context of domain adaptation. To accommodate active
learning and domain adaption, the two naturally different tasks, in a
collaborative framework, we advocate that a customized learning strategy for
the target data is the key to the success of ADA solutions. We present
Divide-and-Adapt (DiaNA), a new ADA framework that partitions the target
instances into four categories with stratified transferable properties. With a
novel data subdivision protocol based on uncertainty and domainness, DiaNA can
accurately recognize the most gainful samples. While sending the informative
instances for annotation, DiaNA employs tailored learning strategies for the
remaining categories. Furthermore, we propose an informativeness score that
unifies the data partitioning criteria. This enables the use of a Gaussian
mixture model (GMM) to automatically sample unlabeled data into the proposed
four categories. Thanks to the "divideand-adapt" spirit, DiaNA can handle data
with large variations of domain gap. In addition, we show that DiaNA can
generalize to different domain adaptation settings, such as unsupervised domain
adaptation (UDA), semi-supervised domain adaptation (SSDA), source-free domain
adaptation (SFDA), etc.
