{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T02:57:21Z","timestamp":1792292241850,"version":"4.4.0"},"reference-count":66,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"5","license":[{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,1]],"date-time":"2023-05-01T00:00:00Z","timestamp":1682899200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation Program of China","doi-asserted-by":"publisher","award":["U1909207"],"award-info":[{"award-number":["U1909207"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation Program of China","doi-asserted-by":"publisher","award":["U21B2029"],"award-info":[{"award-number":["U21B2029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Circuits Syst. Video Technol."],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1109\/tcsvt.2022.3218587","type":"journal-article","created":{"date-parts":[[2022,11,7]],"date-time":"2022-11-07T22:49:39Z","timestamp":1667861379000},"page":"2176-2189","source":"Crossref","is-referenced-by-count":69,"title":["Pull &amp; Push: Leveraging Differential Knowledge Distillation for Efficient Unsupervised Anomaly Detection and Localization"],"prefix":"10.1109","volume":"33","author":[{"given":"Qihang","family":"Zhou","sequence":"first","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1505-6766","authenticated-orcid":false,"given":"Shibo","family":"He","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8998-1217","authenticated-orcid":false,"given":"Haoyu","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0779-9818","authenticated-orcid":false,"given":"Tao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3155-3145","authenticated-orcid":false,"given":"Jiming","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","article-title":"In-painting radiography images for unsupervised anomaly detection","author":"xiang","year":"2021","journal-title":"arXiv 2111 13495"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP42928.2021.9506765"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref56","first-page":"1","article-title":"Learning and evaluating representations for deep one-class classification","author":"sohn","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref15","first-page":"582","article-title":"Support vector method for novelty detection","volume":"12","author":"sch\u00f6lkopf","year":"1999","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref59","article-title":"Student-teacher feature pyramid matching for anomaly detection","author":"wang","year":"2021","journal-title":"arXiv 2103 04257"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1023\/B:MACH.0000008084.60811.49"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539315"},{"key":"ref53","first-page":"9781","article-title":"Deep anomaly detection using geometric transformations","author":"golan","year":"2018","journal-title":"Proc 32nd Int Conf Neural Inf Process Syst"},{"key":"ref52","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"chen","year":"2020","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2015.2492838"},{"key":"ref55","article-title":"Mean-shifted contrastive loss for anomaly detection","author":"reiss","year":"2021","journal-title":"arXiv 2106 03844"},{"key":"ref10","article-title":"A hierarchical spatio&#x2013;temporal graph convolutional neural network for anomaly detection in videos","author":"zeng","year":"2021","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"ref54","first-page":"11839","article-title":"CSI: Novelty detection via contrastive learning on distributionally shifted instances","volume":"33","author":"tack","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref17","article-title":"DFR: Deep feature reconstruction for unsupervised anomaly segmentation","author":"yang","year":"2020","journal-title":"arXiv 2012 07122"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3439950"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412842"},{"key":"ref18","first-page":"161","article-title":"Deep autoencoding models for unsupervised anomaly segmentation in brain MR images","author":"baur","year":"2018","journal-title":"Proc Int MICCAI Brainlesion Workshop"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.100"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00195"},{"key":"ref45","doi-asserted-by":"crossref","first-page":"3521","DOI":"10.1073\/pnas.1611835114","article-title":"Overcoming catastrophic forgetting in neural networks","volume":"114","author":"james","year":"2017","journal-title":"Proc Nat Acad Sci USA"},{"key":"ref48","article-title":"Density estimation using real NVP","author":"dinh","year":"2016","journal-title":"arXiv 1605 08803"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00188"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412109"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00283"},{"key":"ref43","article-title":"Semi-orthogonal embedding for efficient unsupervised anomaly segmentation","author":"kim","year":"2021","journal-title":"arXiv 2105 14737"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00674"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3069254"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2013.2280061"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2929855"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108874"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00951"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2008.2005599"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00678"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"ref35","first-page":"1398","article-title":"Multiscale structural similarity for image quality assessment","volume":"2","author":"wang","year":"2004","journal-title":"Proc 37th Asilomar Conf Signals Syst Comput"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.5220\/0007364503720380"},{"key":"ref37","article-title":"Unsupervised two-stage anomaly detection","author":"liu","year":"2021","journal-title":"arXiv 2103 11671"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107706"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"ref33","article-title":"Iterative energy-based projection on a normal data manifold for anomaly localization","author":"dehaene","year":"2020","journal-title":"arXiv 2002 03734"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00756"},{"key":"ref2","first-page":"1","article-title":"Patch SVDD: Patch-level SVDD for anomaly detection and segmentation","author":"yi","year":"2020","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref39","article-title":"Sub-image anomaly detection with deep pyramid correspondences","author":"cohen","year":"2021","journal-title":"arXiv 2005 02357"},{"key":"ref38","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58520-4_29"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3148392"},{"key":"ref26","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015","journal-title":"ArXiv 1503 02531"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.3039798"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00179"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00057"},{"key":"ref63","first-page":"4393","article-title":"Deep one-class classification","volume":"80","author":"ruff","year":"2018","journal-title":"Proc 35th Mach Learn Res (PMLR)"},{"key":"ref22","first-page":"622","article-title":"GANomaly: Semi-supervised anomaly detection via adversarial training","author":"akcay","year":"2018","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref66","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"tan","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2962229"},{"key":"ref65","first-page":"1","article-title":"Wide residual networks","author":"zagoruyko","year":"2016","journal-title":"Proc Brit Mach Vis Conf"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"ref27","article-title":"FitNets: Hints for thin deep nets","author":"romero","year":"2015","journal-title":"arXiv 1412 6550"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01466"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref62","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref61","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"xiao","year":"2017","journal-title":"ArXiv 1708 07747"}],"container-title":["IEEE Transactions on Circuits and Systems for Video Technology"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/76\/10116045\/09940966.pdf?arnumber=9940966","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T17:45:49Z","timestamp":1685382349000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9940966\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":66,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.1109\/tcsvt.2022.3218587","relation":{},"ISSN":["1051-8215","1558-2205"],"issn-type":[{"value":"1051-8215","type":"print"},{"value":"1558-2205","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5]]}}}