  Supervised learning with large scale labeled datasets and deep layered models
has made a paradigm shift in diverse areas in learning and recognition.
However, this approach still suffers generalization issues under the presence
of a domain shift between the training and the test data distribution. In this
regard, unsupervised domain adaptation algorithms have been proposed to
directly address the domain shift problem. In this paper, we approach the
problem from a transductive perspective. We incorporate the domain shift and
the transductive target inference into our framework by jointly solving for an
asymmetric similarity metric and the optimal transductive target label
assignment. We also show that our model can easily be extended for deep feature
learning in order to learn features which are discriminative in the target
domain. Our experiments show that the proposed method significantly outperforms
state-of-the-art algorithms in both object recognition and digit classification
experiments by a large margin.
