Generative diffusion models: A survey of current theoretical developments

Abstract

Generative diffusion models showed high success in many fields with a powerful theoretical background. They convert the data distribution to noise and remove the noise back to obtain a similar distribution. Many existing reviews focused on the specific application areas without concentrating on the developments about the algorithm. Unlike them we investigated the theoretical developments of the generative diffusion models. These approaches mainly divide into two: training-based and training-free. Awakening to this allowed us a clear and understandable categorization for the researchers who will make new developments in the future.