尹志 (2023-01-31 20:59):
#paper Diffusion Models: A Comprehensive Survey of Methods and Applications, https://doi.org/10.48550/arXiv.2209.00796. 这篇综述对当前非常热门的扩散模型进行了详细的介绍与梳理。文章将当前的扩散模型总结为三类主要模型:DDPMs、SGMs、score SDEs,三类模型逐级一般化,可处理更广泛的问题。除了对三类主流扩散模型进行了详细的讲解,对比,对其相关改进工作进行了梳理,文章还探讨了扩散模型与其它主流的生成模型的联系与区别。文章在最后列举了扩散模型目前在各个领域的应用。考虑到扩散模型受物理概念启发,非常看好其后续结合数学物理的更多推广和应用,比如最近顾险峰老师就在文章中指出基于最优传输的可能改进,这确实是非常有意思的想法和主题。
Diffusion Models: A Comprehensive Survey of Methods and Applications
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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: this https URL.
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