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:无监督域适应(UDA)是一种技术,用于将知识从标签源域向不同但相关的未标签目标域转移。虽然许多UDA方法在过去已经取得成功,但它们往往认为源和目标域必须具有相同的分类标签分布,这可能会限制其在现实世界情景中的有效性。为了解决这一限制,我们提议了一种新的概括化,即通过对源和目标子域对源和目标源分类错误进行重新加权。我们证明,我们提议的概括化约束至少与现实假设下的现有界限一样强大,我们的经验显示,在现实世界数据中,它比现有界限要强得多。我们然后提出一种算法,以尽量减少这种新颖的概括性约束。我们通过数字实验证明,这种方法改善了与最先进的方法相比改变类别分布假设的性能。