  Unsupervised domain adaptation (UDA) aims to transfer the knowledge on a
labeled source domain distribution to perform well on an unlabeled target
domain. Recently, the deep self-training involves an iterative process of
predicting on the target domain and then taking the confident predictions as
hard pseudo-labels for retraining. However, the pseudo-labels are usually
unreliable, and easily leading to deviated solutions with propagated errors. In
this paper, we resort to the energy-based model and constrain the training of
the unlabeled target sample with the energy function minimization objective. It
can be applied as a simple additional regularization. In this framework, it is
possible to gain the benefits of the energy-based model, while retaining strong
discriminative performance following a plug-and-play fashion. We deliver
extensive experiments on the most popular and large scale UDA benchmarks of
image classification as well as semantic segmentation to demonstrate its
generality and effectiveness.
