MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization

Abstract

Domain generalization (DG) aims at learning a model on source domains to well generalize on the unseen target domain. Although it has achieved great success, most of the existing methods require the label information for all training samples in source domains, which is time-consuming and expensive in the real-world application. In this article, we resort to solving the semi-supervised domain generalization (SSDG) task, where there are a few label information in each source domain. To address the task, we first analyze the theory of multi-domain learning, which highlights that (1) mitigating the impact of domain gap and (2) exploiting all samples to train the model can effectively reduce the generalization error in each source domain so as to improve the quality of pseudo-labels. According to the analysis, we propose MultiMatch, i.e., extending FixMatch to the multi-task learning framework, producing the high-quality pseudo-label for SSDG. To be specific, we consider each training domain as a single task (i.e., local task) and combine all training domains together (i.e., global task) to train an extra task for the unseen test domain. In the multi-task framework, we utilize the independent batch normalization and classifier for each task, which can effectively alleviate the interference from different domains during pseudo-labeling. Also, most of the parameters in the framework are shared, which can be trained by all training samples sufficiently. Moreover, to further boost the pseudo-label accuracy and the model’s generalization, we fuse the predictions from the global task and local task during training and testing, respectively. A series of experiments validate the effectiveness of the proposed method, and it outperforms the existing semi-supervised methods and the SSDG method on several benchmark DG datasets.