  Unsupervised domain adaptation has recently emerged as an effective paradigm
for generalizing deep neural networks to new target domains. However, there is
still enormous potential to be tapped to reach the fully supervised
performance. In this paper, we present a novel active learning strategy to
assist knowledge transfer in the target domain, dubbed active domain
adaptation. We start from an observation that energy-based models exhibit
\textit{free energy biases} when training (source) and test (target) data come
from different distributions. Inspired by this inherent mechanism, we
empirically reveal that a simple yet efficient energy-based sampling strategy
sheds light on selecting the most valuable target samples than existing
approaches requiring particular architectures or computation of the distances.
Our algorithm, Energy-based Active Domain Adaptation (EADA), queries groups of
target data that incorporate both domain characteristic and instance
uncertainty into every selection round. Meanwhile, by aligning the free energy
of target data compact around the source domain via a regularization term,
domain gap can be implicitly diminished. Through extensive experiments, we show
that EADA surpasses state-of-the-art methods on well-known challenging
benchmarks with substantial improvements, making it a useful option in the open
world. Code is available at https://github.com/BIT-DA/EADA.
