What is it about?
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.
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Why is it important?
Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications spanning from computer vision, natural language processing, temporal data modeling, to interdisciplinary applications in other scientific disciplines.
Perspectives
This survey provides a thorough investigation into diffusion models, which benefits deep learning researchers.
Ling Yang
Peking University
Read the Original
This page is a summary of: Diffusion Models: A Comprehensive Survey of Methods and Applications, ACM Computing Surveys, November 2023, ACM (Association for Computing Machinery),
DOI: 10.1145/3626235.
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