  Appropriately evaluating the discrepancy between domains is essential for the
success of unsupervised domain adaptation. In this paper, we first point out
that existing discrepancy measures are less informative when complex models
such as deep neural networks are used, in addition to the facts that they can
be computationally highly demanding and their range of applications is limited
only to binary classification. We then propose a novel domain discrepancy
measure, called the paired hypotheses discrepancy (PHD), to overcome these
shortcomings. PHD is computationally efficient and applicable to multi-class
classification. Through generalization error bound analysis, we theoretically
show that PHD is effective even for complex models. Finally, we demonstrate the
practical usefulness of PHD through experiments.
