Target domain pseudo-labelling has shown effectiveness in unsupervised domain adaptation (UDA). However, pseudo-labels of unlabeled target domain data are inevitably noisy due to the distribution shift between source and target domains. This paper proposes a Generative model-based Noise-Robust Training method (GeNRT), which eliminates domain shift while mitigating label noise. GeNRT incorporates a Distribution-based Class-wise Feature Augmentation (D-CFA) and a Generative-Discriminative classifier Consistency (GDC), both based on the class-wise target distributions modelled by generative models. D-CFA minimizes the domain gap by augmenting the source data with distribution-sampled target features, and trains a noise-robust discriminative classifier by using target domain knowledge from the generative models. GDC regards all the class-wise generative models as generative classifiers and enforces a consistency regularization between the generative and discriminative classifiers. It exploits an ensemble of target knowledge from all the generative models to train a noise-robust discriminative classifier and eventually gets theoretically linked to the Ben-David domain adaptation theorem for reducing the domain gap. Extensive experiments on Office-Home, PACS, and Digit-Five show that our GeNRT achieves comparable performance to state-of-the-art methods under single-source and multi-source UDA settings.

翻译：目标域伪标签技术在无监督域适应（UDA）中已展现出有效性。然而，由于源域与目标域之间的分布偏移，未标注目标域数据的伪标签不可避免包含噪声。本文提出一种基于生成模型的噪声鲁棒训练方法（GeNRT），该方法在消除域偏移的同时缓解标签噪声。GeNRT 融合了基于分布的类特征增强（D-CFA）和生成-判别分类器一致性（GDC），两者均基于生成模型建模的逐类目标分布。D-CFA 通过利用分布采样的目标特征增强源数据来缩小域差距，并借助生成模型中的目标域知识训练噪声鲁棒判别分类器。GDC 将所有逐类生成模型视为生成分类器，强制实施生成分类器与判别分类器间的一致性正则化。该方法整合所有生成模型中的目标域集成知识以训练噪声鲁棒判别分类器，最终在理论上与 Ben-David 域适应定理相关联，从而降低域间隙。在 Office-Home、PACS 和 Digit-Five 上的大量实验表明，在单源和多源 UDA 设置下，我们的 GeNRT 性能与最先进方法相当。