927
v1v2v3v4v5v6v7v8v9v10 (latest)

A Survey on Generative Diffusion Model

IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022
Hanqun Cao
Cheng Tan
Pheng-Ann Heng
Stan Z. Li
Abstract

Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion models, recognized as one of the paramount generative models, materialize human ideation into tangible instances across diverse domains, encompassing imagery, text, speech, biology, and healthcare. To provide advanced and comprehensive insights into diffusion, this survey comprehensively elucidates its developmental trajectory and future directions from three distinct angles: the fundamental formulation of diffusion, algorithmic enhancements, and the manifold applications of diffusion. Each layer is meticulously explored to offer a profound comprehension of its evolution. Structured and summarized approaches are presented in https://github.com/chq1155/A-Survey-on-Generative-Diffusion-Model.

View on arXiv
Comments on this paper