Domain Adaptation via Rebalanced Sub-domain Alignment

• URL: http://arxiv.org/abs/2302.02009v1
• Date: Fri, 3 Feb 2023 21:30:40 GMT
• Title: Domain Adaptation via Rebalanced Sub-domain Alignment
• Authors: Yiling Liu, Juncheng Dong, Ziyang Jiang, Ahmed Aloui, Keyu Li, Hunter Klein, Vahid Tarokh, David Carlson
• Abstract summary: Unsupervised domain adaptation (UDA) is a technique used to transfer knowledge from a labeled source domain to a related unlabeled target domain. Many UDA methods have shown success in the past, but they often assume that the source and target domains must have identical class label distributions. We propose a novel generalization bound that reweights source classification error by aligning source and target sub-domains.
• Score: 22.68115322836635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised domain adaptation (UDA) is a technique used to transfer knowledge from a labeled source domain to a different but related unlabeled target domain. While many UDA methods have shown success in the past, they often assume that the source and target domains must have identical class label distributions, which can limit their effectiveness in real-world scenarios. To address this limitation, we propose a novel generalization bound that reweights source classification error by aligning source and target sub-domains. We prove that our proposed generalization bound is at least as strong as existing bounds under realistic assumptions, and we empirically show that it is much stronger on real-world data. We then propose an algorithm to minimize this novel generalization bound. We demonstrate by numerical experiments that this approach improves performance in shifted class distribution scenarios compared to state-of-the-art methods.

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