With the goal of directly generalizing trained models to unseen target domains, domain generalization (DG), a newly proposed learning paradigm, has attracted considerable attention. Previous DG models usually require a sufficient quantity of annotated samples from observed source domains during training. In this paper, we relax this requirement about full annotation and investigate semi-supervised domain generalization (SSDG) where only one source domain is fully annotated along with the other domains totally unlabeled in the training process. With the challenges of tackling the domain gap between observed source domains and predicting unseen target domains, we propose a novel deep framework via joint domain-aware labels and dual-classifier to produce high-quality pseudo-labels. Concretely, to predict accurate pseudo-labels under domain shift, a domain-aware pseudo-labeling module is developed. Also, considering inconsistent goals between generalization and pseudo-labeling: former prevents overfitting on all source domains while latter might overfit the unlabeled source domains for high accuracy, we employ a dual-classifier to independently perform pseudo-labeling and domain generalization in the training process. Extensive results on publicly available DG benchmark datasets show the efficacy of our proposed SSDG method compared to the well-designed baselines and the state-of-the-art semi-supervised learning methods.

本文提出了一种新颖的深度框架，利用半监督域泛化技术，通过联合领域感知标签和双分类器生成高质量的伪标签来解决从观察源域到预测未知目标域之间的领域差异问题，同时当准确的伪标签为无标签源域生成时，使用域混合操作增加标记和未标记域之间的新域，有助于提高模型的泛化能力，并在公开的DG基准数据集上展示了所提出的SSDG方法的有效性。

更好的伪标签:用于半监督领域通用问题的联合领域感知标签和双分类器