  Domain Adaptation aiming to learn a transferable feature between different
but related domains has been well investigated and has shown excellent
empirical performances. Previous works mainly focused on matching the marginal
feature distributions using the adversarial training methods while assuming the
conditional relations between the source and target domain remained unchanged,
$i.e.$, ignoring the conditional shift problem. However, recent works have
shown that such a conditional shift problem exists and can hinder the
adaptation process. To address this issue, we have to leverage labelled data
from the target domain, but collecting labelled data can be quite expensive and
time-consuming. To this end, we introduce a discriminative active learning
approach for domain adaptation to reduce the efforts of data annotation.
Specifically, we propose three-stage active adversarial training of neural
networks: invariant feature space learning (first stage), uncertainty and
diversity criteria and their trade-off for query strategy (second stage) and
re-training with queried target labels (third stage). Empirical comparisons
with existing domain adaptation methods using four benchmark datasets
demonstrate the effectiveness of the proposed approach.
