  A classifier trained on a dataset seldom works on other datasets obtained
under different conditions due to domain shift. This problem is commonly
addressed by domain adaptation methods. In this work we introduce a novel deep
learning framework which unifies different paradigms in unsupervised domain
adaptation. Specifically, we propose domain alignment layers which implement
feature whitening for the purpose of matching source and target feature
distributions. Additionally, we leverage the unlabeled target data by proposing
the Min-Entropy Consensus loss, which regularizes training while avoiding the
adoption of many user-defined hyper-parameters. We report results on publicly
available datasets, considering both digit classification and object
recognition tasks. We show that, in most of our experiments, our approach
improves upon previous methods, setting new state-of-the-art performances.
