While there have been considerable advancements in machine learning driven by
extensive datasets, a significant disparity still persists in the availability
of data across various sources and populations. This inequality across domains
poses challenges in modeling for those with limited data, which can lead to
profound practical and ethical concerns. In this paper, we address a
representative case of data inequality problem across domains termed
Semi-Supervised Domain Generalization (SSDG), in which only one domain is
labeled while the rest are unlabeled. We propose a novel algorithm, ProUD,
which can effectively learn domain-invariant features via domain-aware
prototypes along with progressive generalization via uncertainty-adaptive
mixing of labeled and unlabeled domains. Our experiments on three different
benchmark datasets demonstrate the effectiveness of ProUD, outperforming all
baseline models including single domain generalization and semi-supervised
learning. Source code will be released upon acceptance of the paper.

本文介绍了一种名为 ProUD 的新算法，通过领域感知原型以及标记和未标记领域的不确定性自适应混合，有效地学习领域不变特征，解决了领域之间数据不平等的问题，并在三个不同的基准数据集上进行的实验证明了 ProUD 的有效性，胜过所有基线模型，包括单领域泛化和半监督学习。