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

翻译：超越领域泛化（DG）的目标，视觉识别模型还应通过利用有限标签在学习过程中实现数据高效性。本文研究半监督领域泛化（SSDG）问题，该问题对自动化医疗等实际应用至关重要。SSDG要求在训练数据仅部分标注时，学习跨领域泛化模型。实证研究表明，DG方法在SSDG场景下表现欠佳，这可能是因为它们无法利用未标注数据。半监督学习（SSL）虽有所改进，但其结果仍逊于全监督学习。基于SSL的SSDG最优方法面临的核心挑战是：在多重领域偏移下选择准确的伪标签，以及在有限标签条件下减少对源领域的过拟合。本文提出新的SSDG方法，创新性地引入基于模型平均的不确定性引导伪标签技术（UPLM）。我们提出的不确定性引导伪标签方法（UPL）利用模型不确定性改进伪标签选择，解决了多源未标注数据下模型校准不良的问题。该UPL技术结合新型模型平均（MA）策略，可减轻有限标签下对源领域的过拟合。在多个代表性DG数据集上的广泛实验表明，我们的方法相比现有方法具有显著优势。相关代码及所选标注数据种子已发布于GitHub：https://github.com/Adnan-Khan7/UPLM