Overcoming Data Inequality across Domains with Semi-Supervised Domain
  Generalization
Jinha Park Wonguk Cho Taesup Kim
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.