Abstract

Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation, and molecule design. In this survey, we provide an overview of the rapidly expanding body of work on diffusion models, categorizing the research into three key areas: efficient sampling, improved likelihood estimation, and handling data with special structures. We also discuss the potential for combining diffusion models with other generative models for enhanced results. We further review the wide-ranging applications of diffusion models in fields spanning from computer vision, natural language processing, temporal data modeling, to interdisciplinary applications in other scientific disciplines. This survey aims to provide a contextualized, in-depth look at the state of diffusion models, identifying the key areas of focus and pointing to potential areas for further exploration. Github: https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy

Keywords

Computer scienceData scienceGenerative grammarKey (lock)Focus (optics)Generative modelDiffusionArtificial intelligenceMachine learning

Affiliated Institutions

Related Publications

Publication Info

Year
2023
Type
review
Volume
56
Issue
4
Pages
1-39
Citations
1081
Access
Closed

External Links

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1081
OpenAlex

Cite This

L. Yang, Zhilong Zhang, Yang Song et al. (2023). Diffusion Models: A Comprehensive Survey of Methods and Applications. ACM Computing Surveys , 56 (4) , 1-39. https://doi.org/10.1145/3626235

Identifiers

DOI
10.1145/3626235