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.

翻译：无监督域自适应是一种用于将知识从带标签的源域迁移到不同但相关的无标签目标域的技术。尽管许多域自适应方法在过去已取得成效，但它们通常假设源域与目标域必须具有相同的类别标签分布，这限制了其在真实场景中的有效性。为解决这一局限，我们提出了一种新的泛化界，通过对齐源域与目标域的子域来对源域分类误差进行重加权。我们证明，在合理假设下，所提出的泛化界至少与现有界具有同等强度，并通过实验表明其在真实数据上表现更为优越。进而我们提出一种算法以最小化这一新的泛化界。数值实验表明，在类别分布偏移的场景下，该方法相较当前最优方法能有效提升性能。