  Unsupervised domain adaptation (UDA) aims at inferring class labels for
unlabeled target domain given a related labeled source dataset. Intuitively, a
model trained on source domain normally produces higher uncertainties for
unseen data. In this work, we build on this assumption and propose to adapt
from source to target domain via calibrating their predictive uncertainties.
The uncertainty is quantified as the Renyi entropy, from which we propose a
general Renyi entropy regularization (RER) framework. We further employ
variational Bayes learning for reliable uncertainty estimation. In addition,
calibrating the sample variance of network parameters serves as a plug-in
regularizer for training. We discuss the theoretical properties of the proposed
method and demonstrate its effectiveness on three domain-adaptation tasks.
