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Unsupervised domain adaptation (UDA) is a technique used to transferknowledge from a labeled source domain to a different but related unlabeledtarget domain. While many UDA methods have shown success in the past, theyoften assume that the source and target domains must have identical class labeldistributions, which can limit their effectiveness in real-world scenarios. Toaddress this limitation, we propose a novel generalization bound that reweightssource classification error by aligning source and target sub-domains. We provethat our proposed generalization bound is at least as strong as existing boundsunder realistic assumptions, and we empirically show that it is much strongeron real-world data. We then propose an algorithm to minimize this novelgeneralization bound. We demonstrate by numerical experiments that thisapproach improves performance in shifted class distribution scenarios comparedto state-of-the-art methods.

Domain Adaptation via Rebalanced Sub-domain Alignment