Unsupervised Domain Adaptation Based on Source-guided Discrepancy
This work addresses a key bottleneck in domain adaptation for machine learning practitioners, offering incremental improvements in computational cost and theoretical guarantees.
The paper tackles the problem of measuring differences between source and target domains in unsupervised domain adaptation by proposing a source-guided discrepancy (S-disc) that uses source labels to improve efficiency and tighten generalization error bounds, with experimental results showing advantages over existing methods.
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.