{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T13:17:21Z","timestamp":1783516641645,"version":"3.55.0"},"reference-count":67,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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 of China","doi-asserted-by":"publisher","award":["U21A20484"],"award-info":[{"award-number":["U21A20484"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100022963","name":"Key Research and Development Program of Zhejiang Province","doi-asserted-by":"publisher","award":["2022C01011"],"award-info":[{"award-number":["2022C01011"]}],"id":[{"id":"10.13039\/100022963","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. on Image Process."],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/tip.2023.3293772","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T18:00:48Z","timestamp":1690221648000},"page":"4327-4340","source":"Crossref","is-referenced-by-count":206,"title":["Omni-Frequency Channel-Selection Representations for Unsupervised Anomaly Detection"],"prefix":"10.1109","volume":"32","author":[{"given":"Yufei","family":"Liang","sequence":"first","affiliation":[{"name":"Laboratory of Advanced Perception on Robotics and Intelligent Learning, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8891-6766","authenticated-orcid":false,"given":"Jiangning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Laboratory of Advanced Perception on Robotics and Intelligent Learning, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1017-5897","authenticated-orcid":false,"given":"Shiwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"NetEase Fuxi AI Laboratory, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6986-5825","authenticated-orcid":false,"given":"Runze","family":"Wu","sequence":"additional","affiliation":[{"name":"NetEase Fuxi AI Laboratory, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4822-8939","authenticated-orcid":false,"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Laboratory of Advanced Perception on Robotics and Intelligent Learning, College of Control Science and Engineering, Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuwen","family":"Pan","sequence":"additional","affiliation":[{"name":"Discipline of Control Science and Engineering, School of Information and Electrical Engineering, Hangzhou City University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref12","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":"ref56","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16530"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2017.8296547"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"ref58","article-title":"Improved regularization of convolutional neural networks with cutout","author":"devries","year":"2017","journal-title":"arXiv 1708 04552"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00301"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00057"},{"key":"ref11","article-title":"Sub-image anomaly detection with deep pyramid correspondences","author":"cohen","year":"2020","journal-title":"arXiv 2005 02357"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58589-1_13"},{"key":"ref10","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref54","first-page":"14171","article-title":"Old is gold: Redefining the adversarially learned one-class classifier training paradigm","author":"zaigham zaheer","year":"2020","journal-title":"Proc IEEE\/CVF Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref17","article-title":"Discriminative-generative representation learning for one-class anomaly detection","author":"xia","year":"2021","journal-title":"arXiv 2107 12753"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00136"},{"key":"ref19","first-page":"8330","article-title":"DRÆM—A discriminatively trained reconstruction embedding for surface anomaly detection","author":"zavrtanik","year":"2021","journal-title":"Proc IEEE\/CVF Int Conf Comput Vis (ICCV)"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3036770"},{"key":"ref51","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":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00356"},{"key":"ref46","article-title":"Auto-encoding variational Bayes","volume":"14","author":"kingma","year":"2014","journal-title":"Proc 2nd Int Conf Learn Represent"},{"key":"ref45","first-page":"4139","article-title":"LREN: Low-rank embedded network for sample-free hyperspectral anomaly detection","volume":"35","author":"jiang","year":"2020","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref48","first-page":"1","article-title":"Improving unsupervised defect segmentation by applying structural similarity to autoencoders","volume":"5","author":"bergmann","year":"2019","journal-title":"Proc 14th Int Joint Conf Comput Vis Imag Comput Graph Theory Appl"},{"key":"ref47","first-page":"1","article-title":"Generative adversarial nets","volume":"27","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref42","first-page":"1","article-title":"Deep anomaly detection using geometric transformations","volume":"31","author":"golan","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2012.06.017"},{"key":"ref43","first-page":"11839","article-title":"CSI: Novelty detection via contrastive learning on distributionally shifted instances","author":"tack","year":"2020","journal-title":"Proc 34th Conf Neural Inf Process Syst"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2940686"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3072863"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-019-01476-x"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00865"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2012.2194505"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00678"},{"key":"ref5","first-page":"71","article-title":"Classifier two sample test for video anomaly detections","author":"liu","year":"2018","journal-title":"Proc Brit Mach Vis Conf (BMVC)"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2015.2469136"},{"key":"ref35","article-title":"Deep nearest neighbor anomaly detection","author":"bergman","year":"2020","journal-title":"arXiv 2002 10445"},{"key":"ref34","first-page":"4393","article-title":"Deep one-class classification","author":"ruff","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.cviu.2018.02.006","article-title":"Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes","volume":"172","author":"sabokrou","year":"2018","journal-title":"Comput Vis Image Understand"},{"key":"ref36","first-page":"34","article-title":"One-class SVM for learning in image retrieval","author":"chen","year":"2001","journal-title":"Proc Int Conf Image Process"},{"key":"ref31","first-page":"255","article-title":"Anomaly detection over noisy data using learned probability distributions","author":"eskin","year":"2000","journal-title":"Proc 17th Int Conf Mach Learn"},{"key":"ref30","article-title":"Unsupervised anomaly detection with multi-scale interpolated Gaussian descriptors","author":"chen","year":"2021","journal-title":"arXiv 2101 10043"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ISI.2016.7745472"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1137\/1026034"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00195"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"ref24","first-page":"1","article-title":"Classification-based anomaly detection for general data","author":"bergman","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref23","article-title":"Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?","author":"nazare","year":"2018","journal-title":"arXiv 1811 08495"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2019.2917862"},{"key":"ref26","article-title":"DFR: Deep feature reconstruction for unsupervised anomaly segmentation","author":"yang","year":"2020","journal-title":"arXiv 2012 07122"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412109"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i3.16293"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9412092"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.3390\/app10176085"},{"key":"ref22","first-page":"1","article-title":"Transfer representation-learning for anomaly detection","author":"andrews","year":"2016","journal-title":"Proc JMLR"},{"key":"ref66","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc ICLR"},{"key":"ref21","first-page":"1","article-title":"Skip-ganomaly: Skip connected and adversarially trained encoder–decoder anomaly detection","author":"ak\u00e7ay","year":"2019","journal-title":"Proc Int Joint Conf Neural Netw (IJCNN)"},{"key":"ref65","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"paszke","year":"2019","journal-title":"Proc 33rd Conf Neural Inf Process Syst (NeurIPS)"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/DICTA.2013.6691519"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2021.3096067"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"ref60","article-title":"Weakly supervised learning for industrial optical inspection","volume":"6","author":"wieler","year":"2007","journal-title":"Proc DAGM Symp"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-06430-2_33"},{"key":"ref61","article-title":"Puzzle-AE: Novelty detection in images through solving puzzles","author":"salehi","year":"2020","journal-title":"arXiv 2008 12959"}],"container-title":["IEEE Transactions on Image Processing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/83\/9991910\/10192551.pdf?arnumber=10192551","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T18:13:02Z","timestamp":1692641582000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10192551\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":67,"URL":"https:\/\/doi.org\/10.1109\/tip.2023.3293772","relation":{},"ISSN":["1057-7149","1941-0042"],"issn-type":[{"value":"1057-7149","type":"print"},{"value":"1941-0042","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}