We approach the challenge of addressing semi-supervised domain generalization (SSDG). Specifically, our aim is to obtain a model that learns domain-generalizable features by leveraging a limited subset of labelled data alongside a substantially larger pool of unlabeled data. Existing domain generalization (DG) methods which are unable to exploit unlabeled data perform poorly compared to semi-supervised learning (SSL) methods under SSDG setting. Nevertheless, SSL methods have considerable room for performance improvement when compared to fully-supervised DG training. To tackle this underexplored, yet highly practical problem of SSDG, we make the following core contributions. First, we propose a feature-based conformity technique that matches the posterior distributions from the feature space with the pseudo-label from the model's output space. Second, we develop a semantics alignment loss to learn semantically-compatible representations by regularizing the semantic structure in the feature space. Our method is plug-and-play and can be readily integrated with different SSL-based SSDG baselines without introducing any additional parameters. Extensive experimental results across five challenging DG benchmarks with four strong SSL baselines suggest that our method provides consistent and notable gains in two different SSDG settings.

翻译：我们探讨了半监督领域泛化（SSDG）的挑战。具体而言，我们的目标是利用有限数量的标注数据以及大量未标注数据，学习具有领域泛化能力的特征模型。现有的领域泛化（DG）方法无法利用未标注数据，在SSDG设置下表现劣于半监督学习（SSL）方法。然而，与全监督DG训练相比，SSL方法在性能提升上仍有较大空间。为解决这一未被充分探索但极具实用价值的SSDG问题，我们做出以下核心贡献。首先，我们提出一种基于特征的符合性技术，该技术将特征空间中的后验分布与模型输出空间中的伪标签相匹配。其次，我们开发了一种语义对齐损失函数，通过正则化特征空间中的语义结构来学习语义兼容的表示。我们的方法即插即用，可无缝集成到不同的基于SSL的SSDG基线中，且无需引入额外参数。在五个具有挑战性的DG基准测试上，结合四种强SSL基线的广泛实验结果表明，我们的方法在两种不同的SSDG设置下均能带来一致且显著的性能提升。