We consider the problem of active domain adaptation (ADA) to unlabeled target
data, of which subset is actively selected and labeled given a budget
constraint. Inspired by recent analysis on a critical issue from label
distribution mismatch between source and target in domain adaptation, we devise
a method that addresses the issue for the first time in ADA. At its heart lies
a novel sampling strategy, which seeks target data that best approximate the
entire target distribution as well as being representative, diverse, and
uncertain. The sampled target data are then used not only for supervised
learning but also for matching label distributions of source and target
domains, leading to remarkable performance improvement. On four public
benchmarks, our method substantially outperforms existing methods in every
adaptation scenario.

提出了一种主动域自适应的方法，通过新的采样策略，在满足代表性、多样性和不确定性的同时，选择最能近似整个目标分布的样本，并使用这些样本进行监督学习以及匹配源域和目标域的标签分布，取得了显著的性能提升。在四个公共基准测试上，本方法在每种自适应情景下均显著优于现有方法。