Domain Discrepancy Measure Using Complex Models in Unsupervised Domain Adaptation

We address the problem of measuring the difference between two domains in unsupervised domain adaptation. We point out that the existing discrepancy measures are less informative when complex models such as deep neural networks are applied. Furthermore, estimation of the existing discrepancy measures can be computationally difficult and only limited to the binary classification task. To mitigate these shortcomings, we propose a novel discrepancy measure that is very simple to estimate for many tasks not limited to binary classification, theoretically-grounded, and can be applied effectively for complex models. We also provide easy-to-interpret generalization bounds that explain the effectiveness of a family of pseudo-labeling methods in unsupervised domain adaptation. Finally, we conduct experiments to validate the usefulness of our proposed discrepancy measure.