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.

本文提出了一种基于源域推广到目标域的无监督领域适应方法，该方法通过校准源域和目标域的预测不确定性（以 Renyi 熵度量）来实现，使用变分贝叶斯学习可靠的不确定性估计，并讨论了其理论性质并在实验中验证了其有效性。