  Domain adaptation aims to use training data from one or multiple source
domains to learn a hypothesis that can be generalized to a different, but
related, target domain. As such, having a reliable measure for evaluating the
discrepancy of both marginal and conditional distributions is crucial. We
introduce Cauchy-Schwarz (CS) divergence to the problem of unsupervised domain
adaptation (UDA). The CS divergence offers a theoretically tighter
generalization error bound than the popular Kullback-Leibler divergence. This
holds for the general case of supervised learning, including multi-class
classification and regression. Furthermore, we illustrate that the CS
divergence enables a simple estimator on the discrepancy of both marginal and
conditional distributions between source and target domains in the
representation space, without requiring any distributional assumptions. We
provide multiple examples to illustrate how the CS divergence can be
conveniently used in both distance metric- or adversarial training-based UDA
frameworks, resulting in compelling performance.
