  Domain adaptation (DA) enables knowledge transfer from a labeled source
domain to an unlabeled target domain by reducing the cross-domain distribution
discrepancy. Most prior DA approaches leverage complicated and powerful deep
neural networks to improve the adaptation capacity and have shown remarkable
success. However, they may have a lack of applicability to real-world
situations such as real-time interaction, where low target inference latency is
an essential requirement under limited computational budget. In this paper, we
tackle the problem by proposing a dynamic domain adaptation (DDA) framework,
which can simultaneously achieve efficient target inference in low-resource
scenarios and inherit the favorable cross-domain generalization brought by DA.
In contrast to static models, as a simple yet generic method, DDA can integrate
various domain confusion constraints into any typical adaptive network, where
multiple intermediate classifiers can be equipped to infer "easier" and
"harder" target data dynamically. Moreover, we present two novel strategies to
further boost the adaptation performance of multiple prediction exits: 1) a
confidence score learning strategy to derive accurate target pseudo labels by
fully exploring the prediction consistency of different classifiers; 2) a
class-balanced self-training strategy to explicitly adapt multi-stage
classifiers from source to target without losing prediction diversity.
Extensive experiments on multiple benchmarks are conducted to verify that DDA
can consistently improve the adaptation performance and accelerate target
inference under domain shift and limited resources scenarios
