Semi-Supervised Conditional Diffusion via Label Augmentation
Conditional diffusion models have become a powerful and flexible framework for learning complex conditional distributions from labeled data. In practice, however, acquiring high-quality labels is costly and time-consuming, leaving large volumes of unlabeled data unused. To address this, we introduce label-augmented conditional diffusion (LACD), a simple and effective approach that incorporates unlabeled examples by assigning them a designated trivial label and performing joint denoising score matching over the augmented dataset. We provide sufficient conditions guaranteeing population-level identifiability of the target conditional distribution under this scheme. Moreover, we establish ri...
| Authors | Jin Su, Yuan Gao, Yong Zhou et al. |
|---|---|
| Primary category | stat.ML |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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