{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T19:25:52Z","timestamp":1771961152004,"version":"3.50.1"},"reference-count":92,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"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":["61922088"],"award-info":[{"award-number":["61922088"]}],"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":["62006236"],"award-info":[{"award-number":["62006236"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NUDT Research Project","award":["ZK20-10"],"award-info":[{"award-number":["ZK20-10"]}]},{"name":"HPCL Autonomous","award":["202101-15"],"award-info":[{"award-number":["202101-15"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2023,3,1]]},"DOI":"10.1109\/tpami.2022.3188763","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T19:35:03Z","timestamp":1657136103000},"page":"2952-2969","source":"Crossref","is-referenced-by-count":14,"title":["E3 Outlier: a Self-Supervised Framework for Unsupervised Deep Outlier Detection"],"prefix":"10.1109","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8134-9508","authenticated-orcid":false,"given":"Siqi","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology (NUDT), Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8843-0755","authenticated-orcid":false,"given":"Yijie","family":"Zeng","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7995-6758","authenticated-orcid":false,"given":"Guang","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology (NUDT), Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8217-9600","authenticated-orcid":false,"given":"Zhen","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology (NUDT), Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9066-1475","authenticated-orcid":false,"given":"Xinwang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology (NUDT), Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1491-4594","authenticated-orcid":false,"given":"Sihang","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Technology, NUDT, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2305-7555","authenticated-orcid":false,"given":"En","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, National University of Defense Technology (NUDT), Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marius","family":"Kloft","sequence":"additional","affiliation":[{"name":"Department of Computer Science, TU Kaiserslautern, Kaiserslautern, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5474-4764","authenticated-orcid":false,"given":"Jianping","family":"Yin","sequence":"additional","affiliation":[{"name":"Dongguan University of Technology, Dongguan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1012-5301","authenticated-orcid":false,"given":"Qing","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institue of Technology (Shenzhen), Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3381028"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2017.12.030"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2561288"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2744619"},{"key":"ref7","article-title":"One-class classification","author":"Tax","year":"2001"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.483"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.177"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46454-1_21"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3240508.3240615"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015313"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.1999.790410"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5540018"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-47578-3_1"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2932769"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3439950"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref20","first-page":"5962","article-title":"Effective end-to-end unsupervised outlier detection via inlier priority of discriminative network","volume-title":"Proc. 33rd Int. Conf. Neural Inf. Process. Syst.","author":"Wang"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335437"},{"key":"ref22","first-page":"392","article-title":"Algorithms for mining distancebased outliers in large datasets","volume-title":"Proc. Int. Conf. Very Large Data Bases","author":"Knox"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/335191.335388"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-47887-6_53"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1145\/1645953.1646195"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2014.70"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2015.62"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611972795.13"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.14257\/ijca.2015.8.8.17"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73499-4_6"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-013-0712-0"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.10.009"},{"key":"ref33","first-page":"421","article-title":"Scalable kernel density estimation-based local outlier detection over large data streams","volume-title":"Proc. Annu. Int. Conf. Extending Database Technol.","author":"Qin"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(00)00131-8"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(03)00003-5"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498276"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93037-4_40"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0020217"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.11"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.17"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2947676"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2011.5767852"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-015-5521-0"},{"key":"ref44","article-title":"Deep learning for anomaly detection: A survey","author":"Chalapathy","year":"2019"},{"key":"ref45","first-page":"1100","article-title":"Deep structured energy based models for anomaly detection","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhai"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098052"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71249-9_3"},{"key":"ref48","article-title":"Deep autoencoding gaussian mixture model for unsupervised anomaly detection","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zong"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220042"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2905606"},{"key":"ref51","first-page":"53","article-title":"Generative adversarial networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref52","article-title":"Robust subspace recovery layer for unsupervised anomaly detection","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Lai"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2019.8759553"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.436"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2019.00996"},{"key":"ref56","article-title":"Unsupervised representation learning by predicting image rotations","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Gidaris"},{"key":"ref57","first-page":"9758","article-title":"Deep anomaly detection using geometric transformations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Golan"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2873701"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_32"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2992393"},{"key":"ref63","first-page":"7047","article-title":"Predictive uncertainty estimation via prior networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Malinin"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045502"},{"key":"ref65","first-page":"6402","article-title":"Simple and scalable predictive uncertainty estimation using deep ensembles","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Lakshminarayanan"},{"key":"ref66","article-title":"A baseline for detecting misclassified and out-of-distribution examples in neural networks","author":"Hendrycks","year":"2017","journal-title":"Proc. Int. Conf. Learn. Representations"},{"key":"ref67","first-page":"13969","article-title":"Can you trust your models uncertainty? evaluating predictive uncertainty under dataset shift","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Snoek"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-3994-4"},{"key":"ref69","first-page":"1558","article-title":"Autoencoding beyond pixels using a learned similarity metric","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Larsen","year":"2016"},{"key":"ref70","first-page":"658","article-title":"Generating images with perceptual similarity metrics based on deep networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Dosovitskiy"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/tkde.2021.3130191"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/72.286891"},{"key":"ref74","first-page":"1321","article-title":"On calibration of modern neural networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Guo"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45014-9_1"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525087"},{"key":"ref78","first-page":"11839","article-title":"CSI: Novelty detection via contrastive learning on distributionally shifted instances","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Tack"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref80","article-title":"Fashion-mnist: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2047"},{"key":"ref81","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref82","article-title":"Reading digits in natural images with unsupervised feature learning","author":"Netzer","year":"2011"},{"key":"ref83","doi-asserted-by":"publisher","DOI":"10.1145\/1143844.1143874"},{"key":"ref84","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21735-7_7"},{"key":"ref85","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref86","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3040591"},{"key":"ref87","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2017.315"},{"key":"ref88","article-title":"Classifier two sample test for video anomaly detections","volume-title":"Proc. Brit. Mach. Vis. Conf.","author":"Liu"},{"key":"ref89","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01219"},{"key":"ref90","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413973"},{"key":"ref91","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"ref92","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.338"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/10036240\/09816125.pdf?arnumber=9816125","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T13:17:17Z","timestamp":1719407837000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9816125\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,1]]},"references-count":92,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3188763","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,1]]}}}