{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T19:28:32Z","timestamp":1784402912908,"version":"3.55.0"},"reference-count":290,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Comput. Surv."],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>\n            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:\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/YangLing0818\/Diffusion-Models-Papers-Survey-Taxonomy\">https:\/\/github.com\/YangLing0818\/Diffusion-Models-Papers-Survey-Taxonomy<\/jats:ext-link>\n          <\/jats:p>","DOI":"10.1145\/3626235","type":"journal-article","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T08:56:52Z","timestamp":1696064212000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1309,"title":["Diffusion Models: A Comprehensive Survey of Methods and Applications"],"prefix":"10.1145","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1905-8053","authenticated-orcid":false,"given":"Ling","family":"Yang","sequence":"first","affiliation":[{"name":"Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9307-8440","authenticated-orcid":false,"given":"Zhilong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3193-1679","authenticated-orcid":false,"given":"Yang","family":"Song","sequence":"additional","affiliation":[{"name":"OpenAI, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7521-5127","authenticated-orcid":false,"given":"Shenda","family":"Hong","sequence":"additional","affiliation":[{"name":"Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7375-9833","authenticated-orcid":false,"given":"Runsheng","family":"Xu","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3401-4921","authenticated-orcid":false,"given":"Yue","family":"Zhao","sequence":"additional","affiliation":[{"name":"University of Southern California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7532-5550","authenticated-orcid":false,"given":"Wentao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Mila - Qu\u00e9bec AI Institute, HEC Montr\u00e9al, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1681-4677","authenticated-orcid":false,"given":"Bin","family":"Cui","sequence":"additional","affiliation":[{"name":"Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4848-2304","authenticated-orcid":false,"given":"Ming-Hsuan","family":"Yang","sequence":"additional","affiliation":[{"name":"University of California at Merced and Yonsei University, USA and Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,11,9]]},"reference":[{"key":"e_1_3_1_2_2","article-title":"Diffusion-based time series imputation and forecasting with structured state space models","author":"Alcaraz Juan Miguel Lopez","year":"2022","unstructured":"Juan Miguel Lopez Alcaraz and Nils Strodthoff. 2022. 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