Diffusion Models: A Comprehensive Survey of Methods and Applications

TL;DR

This survey comprehensively reviews diffusion models, highlighting their methods, applications, and potential for future research across various fields including vision, language, and scientific disciplines.

Contribution

It categorizes recent diffusion model research into key areas and discusses their integration with other generative approaches, providing a structured overview of the field.

Findings

Diffusion models achieve state-of-the-art results in image and video synthesis.

They are effectively applied in molecule design and interdisciplinary scientific tasks.

The survey identifies promising directions for combining diffusion models with other techniques.

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 generation, 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.