FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization

• URL: http://arxiv.org/abs/2506.20841v1
• Date: Wed, 25 Jun 2025 21:25:05 GMT
• Title: FixCLR: Negative-Class Contrastive Learning for Semi-Supervised Domain Generalization
• Authors: Ha Min Son, Shahbaz Rezaei, Xin Liu,
• Abstract summary: Due to label scarcity, applying domain generalization methods often underperform.<n>We introduce FixCLR, which explicitly regularize to learn domains invariant representations across all domains.<n>Our research includes extensive experiments that have not been previously explored in SSDG studies.
• Score: 6.683066713491661
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semi-supervised domain generalization (SSDG) aims to solve the problem of generalizing to out-of-distribution data when only a few labels are available. Due to label scarcity, applying domain generalization methods often underperform. Consequently, existing SSDG methods combine semi-supervised learning methods with various regularization terms. However, these methods do not explicitly regularize to learn domains invariant representations across all domains, which is a key goal for domain generalization. To address this, we introduce FixCLR. Inspired by success in self-supervised learning, we change two crucial components to adapt contrastive learning for explicit domain invariance regularization: utilization of class information from pseudo-labels and using only a repelling term. FixCLR can also be added on top of most existing SSDG and semi-supervised methods for complementary performance improvements. Our research includes extensive experiments that have not been previously explored in SSDG studies. These experiments include benchmarking different improvements to semi-supervised methods, evaluating the performance of pretrained versus non-pretrained models, and testing on datasets with many domains. Overall, FixCLR proves to be an effective SSDG method, especially when combined with other semi-supervised methods.

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