Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data

arXiv:2404.10177v1 Announce Type: cross
Abstract: Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data. Both Ambient Diffusion and alternative SURE-based approaches for learning diffusion models from corrupted data resort to approximations which deteriorate performance. We present the first framework for training diffusion models that provably sample from the uncorrupted distribution given only noisy training data, solving an open problem in this space. Our key technical contribution is a method that uses a double application of …

abstract ambient arxiv consistent corrupted data cs.ai cs.cv cs.lg data diffusion diffusion models framework performance training type