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.

arXiv commentTo appear in AAAI-19

References in corpus (6)

• Scikit-learn: Machine Learning in Python
• Domain Adaptation: Learning Bounds and Algorithms
• Asymmetric Tri-training for Unsupervised Domain Adaptation
• Joint Distribution Optimal Transportation for Domain Adaptation
• Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
• New Analysis and Algorithm for Learning with Drifting Distributions

Cited by in corpus (1)

• Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback