  Domain adaptation investigates the problem of leveraging knowledge from a
well-labeled source domain to an unlabeled target domain, where the two domains
are drawn from different data distributions. Because of the distribution
shifts, different target samples have distinct degrees of difficulty in
adaptation. However, existing domain adaptation approaches overwhelmingly
neglect the degrees of difficulty and deploy exactly the same framework for all
of the target samples. Generally, a simple or shadow framework is fast but
rough. A sophisticated or deep framework, on the contrary, is accurate but
slow. In this paper, we aim to challenge the fundamental contradiction between
the accuracy and speed in domain adaptation tasks. We propose a novel approach,
named {\it agile domain adaptation}, which agilely applies optimal frameworks
to different target samples and classifies the target samples according to
their adaptation difficulties. Specifically, we propose a paradigm which
performs several early detections before the final classification. If a sample
can be classified at one of the early stage with enough confidence, the sample
would exit without the subsequent processes. Notably, the proposed method can
significantly reduce the running cost of domain adaptation approaches, which
can extend the application scenarios of domain adaptation to even mobile
devices and real-time systems. Extensive experiments on two open benchmarks
verify the effectiveness and efficiency of the proposed method.
