Multimodal Deep Generative Model for Semi-Supervised Learning under Class Imbalance
When modeling class-imbalanced data, it is crucial to address the imbalance, as models trained on such data tend to be biased towards the majority classes. This problem is amplified under partial supervision, where pseudo-labels for unlabeled data are predicted based on imbalanced labeled data, propagating the bias. While recent semi-supervised models address class imbalance, they typically assume single-modal input data. However, with the growing availability of multimodal data, it is essential to leverage complementary modalities. In this article, we propose a multimodal deep generative model for semi-supervised learning under class imbalance. Our approach uses separate encoders for eac...
| Authors | Heegeon Yoon, Heeyoung Kim |
|---|---|
| Primary category | stat.ML |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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