MOOD: Leveraging Out-of-Distribution Data to Enhance Imbalanced Semi-Supervised Learning.

The imbalanced semi-supervised learning (SSL) has emerged as a critical research area due to the prevalence of class imbalanced and partially labeled data in real-world scenarios. As the requirement for data volume increases, naturally collected datasets inevitably contain out-of-distribution (OOD) samples. However, the performance of existing imbalanced SSL methods experiences a marked deterioration with OOD data. In this article, we propose an imbalanced SSL method called mixup-OOD (MOOD) to address this issue. The core idea is to "turn waste into treasure," exploring the potential of leveraging seemingly detrimental OOD data to expand the feature space, particularly for tail classes. Specifically, we first filter OOD data from unlabeled data, and then fuse it with labeled data to boost feature diversity for the tail classes. To avoid feature overlapping with OOD data, we develop a push-and-pull (PaP) loss to attract in-distribution (ID) instances toward respective class centroids while repelling OOD samples from them. Extensive experiments show that MOOD achieves superior performance compared with other state-of-the-art methods and exhibits robustness across data with different imbalanced ratios and OOD proportions. The source code is available at: https://github.com/xlhuang132/MOODv2.