Unsupervised Domain Adaptation Based on Source-guided Discrepancy

Understand

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.