MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization
This work addresses the challenge of reducing labeling costs for domain generalization, but it is incremental as it builds on existing methods like FixMatch.
The paper tackles the problem of semi-supervised domain generalization (SSDG), where limited labeled data is available in source domains, by proposing MultiMatch, a method that extends FixMatch into a multi-task learning framework to generate high-quality pseudo-labels. The result shows that MultiMatch outperforms existing semi-supervised and SSDG methods on benchmark datasets.
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 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 paper, 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 the 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 BN and classifier for each task, which can effectively alleviate the interference from different domains during pseudo-labeling. Also, most of 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.