{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:29:22Z","timestamp":1775665762431,"version":"3.50.1"},"reference-count":55,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,10,19]],"date-time":"2025-10-19T00:00:00Z","timestamp":1760832000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,19]]},"DOI":"10.1109\/iccvw69036.2025.00157","type":"proceedings-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T20:44:02Z","timestamp":1771879442000},"page":"1473-1482","source":"Crossref","is-referenced-by-count":1,"title":["RDDPM: Robust Denoising Diffusion Probabilistic Model for Unsupervised Anomaly Segmentation"],"prefix":"10.1109","author":[{"given":"Mehrdad","family":"Moradi","sequence":"first","affiliation":[{"name":"H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology,Atlanta,Georgia"}]},{"given":"Kamran","family":"Paynabar","sequence":"additional","affiliation":[{"name":"H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology,Atlanta,Georgia"}]}],"member":"263","reference":[{"key":"ref1","volume-title":"Cvpr 2023 tutorial on diffusion models","year":"2025"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP46576.2022.9897283"},{"key":"ref3","author":"Arjovsky","year":"2017","journal-title":"Wasserstein gan"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-11723-8_16"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.5220\/0007364500002108"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3116668"},{"key":"ref8","volume-title":"Large scale gan training for high fidelity natural image synthesis","author":"Brock","year":"2019"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/1970392.1970395"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.14778\/3632093.3632101"},{"key":"ref11","volume-title":"Diffusion models beat gans on image synthesis","author":"Dhariwal","year":"2021"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72761-0_6"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00179"},{"key":"ref14","author":"Goodfellow","year":"2014","journal-title":"Gener-ative adversarial networks"},{"key":"ref15","author":"Gulrajani","year":"2017","journal-title":"Improved training of wasserstein gans"},{"key":"ref16","first-page":"6840","article-title":"Denoising diffusion proba-bilistic models","author":"Ho","year":"2020","journal-title":"Neural Information Processing Systems (NeurIPS)"},{"key":"ref17","article-title":"Cascaded diffusion models for high fidelity image generation","author":"Ho","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177703732"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-45673-2_37"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-45673-2_37"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2020.2970919"},{"key":"ref22","article-title":"Improving diffusion models\u2019 data-corruption resistance using scheduled pseudo-huber loss","author":"Khrapov","year":"2024","journal-title":"arXiv preprint"},{"key":"ref23","author":"Kingma","year":"2023","journal-title":"Variational diffusion models"},{"key":"ref24","author":"Kong","year":"2021","journal-title":"On fast sampling of diffusion probabilistic models"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2019.00224"},{"key":"ref26","author":"Livernoche","year":"2023","journal-title":"On diffusion modeling for anomaly detection"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00522"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1080\/24725854.2022.2163435"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-85181-0_12"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.278"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr52688.2022.01042"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1002\/SERIES1345"},{"key":"ref35","author":"San-Roman","year":"2021","journal-title":"Noise estimation for generative diffusion models"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.01.010"},{"key":"ref38","article-title":"Denoising diffusion implicit models","volume-title":"International Conference on Learning Representations (ICLR)","author":"Song"},{"key":"ref39","author":"Song","year":"2021","journal-title":"Score-based generative modeling through stochastic differential equations"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00398"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"ref42","author":"Vahdat","year":"2021","journal-title":"Score-based generative modeling in latent space"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00080"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00080"},{"key":"ref45","author":"Xiao","year":"2022","journal-title":"Tackling the generative learning trilemma with denoising diffusion gans"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2010.2048503"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2015.1102764"},{"key":"ref48","article-title":"Tabadm: Unsupervised tabular anomaly detection with diffusion models","author":"Zamberg","year":"2023","journal-title":"arXiv preprint"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107706"},{"key":"ref50","author":"Zenati","year":"2024","journal-title":"Efficient gan-based anomaly detection"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/tpami.2025.3570494"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00624"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01580"},{"key":"ref54","author":"Zhao","year":"2024","journal-title":"Bias and generalization in deep generative models: An empirical study"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098052"}],"event":{"name":"2025 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW)","location":"Honolulu, HI, USA","start":{"date-parts":[[2025,10,19]]},"end":{"date-parts":[[2025,10,20]]}},"container-title":["2025 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11373940\/11374285\/11375667.pdf?arnumber=11375667","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T07:46:16Z","timestamp":1771919176000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11375667\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,19]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/iccvw69036.2025.00157","relation":{},"subject":[],"published":{"date-parts":[[2025,10,19]]}}}