  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.
