  Beyond attaining domain generalization (DG), visual recognition models should
also be data-efficient during learning by leveraging limited labels. We study
the problem of Semi-Supervised Domain Generalization (SSDG) which is crucial
for real-world applications like automated healthcare. SSDG requires learning a
cross-domain generalizable model when the given training data is only partially
labelled. Empirical investigations reveal that the DG methods tend to
underperform in SSDG settings, likely because they are unable to exploit the
unlabelled data. Semi-supervised learning (SSL) shows improved but still
inferior results compared to fully-supervised learning. A key challenge, faced
by the best-performing SSL-based SSDG methods, is selecting accurate
pseudo-labels under multiple domain shifts and reducing overfitting to source
domains under limited labels. In this work, we propose new SSDG approach, which
utilizes a novel uncertainty-guided pseudo-labelling with model averaging
(UPLM). Our uncertainty-guided pseudo-labelling (UPL) uses model uncertainty to
improve pseudo-labelling selection, addressing poor model calibration under
multi-source unlabelled data. The UPL technique, enhanced by our novel model
averaging (MA) strategy, mitigates overfitting to source domains with limited
labels. Extensive experiments on key representative DG datasets suggest that
our method demonstrates effectiveness against existing methods. Our code and
chosen labelled data seeds are available on GitHub:
https://github.com/Adnan-Khan7/UPLM
