Title: Unsupervised Domain Adaptation Based on Source-guided Discrepancy,
Abstract: Unsupervised domain adaptation is the problem setting where data generating
distributions in the source and target domains are different, and labels in the
target domain are unavailable. One important question in unsupervised domain
adaptation is how to measure the difference between the source and target
domains. A previously proposed discrepancy that does not use the source domain
labels requires high computational cost to estimate and may lead to a loose
generalization error bound in the target domain. To mitigate these problems, we
propose a novel discrepancy called source-guided discrepancy (S-disc), which
exploits labels in the source domain. As a consequence, S-disc can be computed
efficiently with a finite sample convergence guarantee. In addition, we show
that S-disc can provide a tighter generalization error bound than the one based
on an existing discrepancy. Finally, we report experimental results that
demonstrate the advantages of S-disc over the existing discrepancies.