Paper Proposes Theoretical Framework for Memorization in Diffusion Models

A new arXiv preprint develops a theoretical account of why diffusion models sometimes reproduce training data verbatim rather than generating novel samples. The authors propose smoothing the score function as a way to reduce this memorization effect and improve generalization. The work is presented as an explanation of the phenomenon rather than a new model release.