  Generalizing deep neural networks to new target domains is critical to their
real-world utility. In practice, it may be feasible to get some target data
labeled, but to be cost-effective it is desirable to select a
maximally-informative subset via active learning (AL). We study the problem of
AL under a domain shift, called Active Domain Adaptation (Active DA). We
demonstrate how existing AL approaches based solely on model uncertainty or
diversity sampling are less effective for Active DA. We propose Clustering
Uncertainty-weighted Embeddings (CLUE), a novel label acquisition strategy for
Active DA that performs uncertainty-weighted clustering to identify target
instances for labeling that are both uncertain under the model and diverse in
feature space. CLUE consistently outperforms competing label acquisition
strategies for Active DA and AL across learning settings on 6 diverse domain
shifts for image classification.
