  We propose an active learning approach for transferring representations
across domains. Our approach, active adversarial domain adaptation (AADA),
explores a duality between two related problems: adversarial domain alignment
and importance sampling for adapting models across domains. The former uses a
domain discriminative model to align domains, while the latter utilizes it to
weigh samples to account for distribution shifts. Specifically, our importance
weight promotes samples with large uncertainty in classification and diversity
from labeled examples, thus serves as a sample selection scheme for active
learning. We show that these two views can be unified in one framework for
domain adaptation and transfer learning when the source domain has many labeled
examples while the target domain does not. AADA provides significant
improvements over fine-tuning based approaches and other sampling methods when
the two domains are closely related. Results on challenging domain adaptation
tasks, e.g., object detection, demonstrate that the advantage over baseline
approaches is retained even after hundreds of examples being actively
annotated.
