{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T10:01:25Z","timestamp":1782208885657,"version":"3.54.5"},"reference-count":48,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61733013"],"award-info":[{"award-number":["61733013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62073245"],"award-info":[{"award-number":["62073245"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Instrum. Meas."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tim.2021.3107586","type":"journal-article","created":{"date-parts":[[2021,8,24]],"date-time":"2021-08-24T19:58:14Z","timestamp":1629835094000},"page":"1-12","source":"Crossref","is-referenced-by-count":43,"title":["Unsupervised Anomaly Segmentation Via Multilevel Image Reconstruction and Adaptive Attention-Level Transition"],"prefix":"10.1109","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8579-4211","authenticated-orcid":false,"given":"Yi","family":"Yan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3486-4176","authenticated-orcid":false,"given":"Deming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7845-7068","authenticated-orcid":false,"given":"Guangliang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5644-1188","authenticated-orcid":false,"given":"Qijun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","author":"kendall","year":"2017","journal-title":"arXiv 1703 04977"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref33","first-page":"694","article-title":"Perceptual losses for real-time style transfer and super-resolution","author":"johnson","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00325"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.12.024"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00457"},{"key":"ref37","first-page":"658","article-title":"Generating images with perceptual similarity metrics based on deep networks","author":"dosovitskiy","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107706"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00917"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref10","first-page":"1","article-title":"A novel pixel-wise defect inspection method based on stable background reconstruction","volume":"70","author":"lv","year":"2021","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref40","first-page":"1050","article-title":"Dropout as a Bayesian approximation: Representing model uncertainty in deep learning","volume":"48","author":"gal","year":"2016","journal-title":"Proc 33rd Int Conf Mach Learn (PMLR)"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.5220\/0007364503720380"},{"key":"ref12","first-page":"485","article-title":"Attention guided anomaly localization in images","author":"venkataramanan","year":"2020","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref13","article-title":"Iterative energy-based projection on a normal data manifold for anomaly localization","author":"dehaene","year":"2020","journal-title":"arXiv 2002 03734"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2019.01.010"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"ref17","article-title":"Patch SVDD: Patch-level SVDD for anomaly detection and segmentation","author":"yi","year":"2020","journal-title":"Proc Asian Conf Comput Vis"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1023\/B:MACH.0000008084.60811.49"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00753"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00114"},{"key":"ref27","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref3","first-page":"11507","article-title":"Where&#x2019;s wally now? Deep generative and discriminative embeddings for novelty detection","author":"burlina","year":"2019","journal-title":"Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2917862"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01371"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00301"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2021.3056653"},{"key":"ref7","first-page":"4393","article-title":"Deep one-class classification","author":"ruff","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/INTELES.2014.7008985"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1109\/TIM.2018.2795178","article-title":"An unsupervised-learning-based approach for automated defect inspection on textured surfaces","volume":"67","author":"shuang","year":"2018","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01181"},{"key":"ref46","article-title":"Why do deep convolutional networks generalize so poorly to small image transformations?","author":"azulay","year":"2018","journal-title":"arXiv 1805 12177"},{"key":"ref20","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299064"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00867"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref47","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":"ref21","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2013","journal-title":"arXiv 1312 6114"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref24","article-title":"Sub-image anomaly detection with deep pyramid correspondences","author":"cohen","year":"2020","journal-title":"arXiv 2005 02357"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2919951"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.167"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.11.018"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"}],"container-title":["IEEE Transactions on Instrumentation and Measurement"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/19\/9259274\/09521893.pdf?arnumber=9521893","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T22:10:12Z","timestamp":1636409412000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9521893\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":48,"URL":"https:\/\/doi.org\/10.1109\/tim.2021.3107586","relation":{},"ISSN":["0018-9456","1557-9662"],"issn-type":[{"value":"0018-9456","type":"print"},{"value":"1557-9662","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}