Semi-Supervised Conditional Diffusion via Label Augmentation
Researchers propose label-augmented conditional diffusion (LACD), a method that incorporates unlabeled data into conditional diffusion models by assigning a designated trivial label and performing joint denoising score matching. Theoretical analysis provides sufficient conditions for identifiability and shows that, with enough unlabeled data, LACD achieves faster convergence in total variation distance compared to purely supervised approaches. Experiments on synthetic, image, and tabular data demonstrate improved sample efficiency and generative performance.
Why it matters: This work provides a principled approach to leveraging unlabeled data for conditional diffusion models, potentially reducing reliance on costly labeled datasets.
Full story at: arXiv Statistical ML ↗