Semi-Supervised Domain Generalization with Graph-Based Classifier

Abstract

Semi-supervised domain generalization (SSDG) has recently emerged as a potential research topic. Compared to domain generalization, SSDG represents a realistic and challenging goal, which only requires a few labels from source domains. To tackle this problem, this work presents a novel pseudo-labeling method that facilitates incremental learning on a large amount of unlabeled data. With edge weighting optimization, the proposed method utilizes the graph Laplacian regularizer (GLR) in a multi-class setting that relies on the generated similarity graph. The proposed overall SSDG scheme mitigates the overfitting problem by an adaptive threshold module based on a two-stage GLR denoiser. Our experiments on PACS and OfficeHome verify that the proposed method effectively improves the quality of pseudo-labeling and domain generalization, achieving top performance in terms of accuracy.