{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:21:13Z","timestamp":1783610473271,"version":"3.55.0"},"reference-count":72,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"2","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"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":["IEEE Trans. Neural Netw. Learning Syst."],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1109\/tnnls.2020.2979049","type":"journal-article","created":{"date-parts":[[2020,4,7]],"date-time":"2020-04-07T21:55:02Z","timestamp":1586296502000},"page":"675-684","source":"Crossref","is-referenced-by-count":90,"title":["Deep End-to-End One-Class Classifier"],"prefix":"10.1109","volume":"32","author":[{"given":"Mohammad","family":"Sabokrou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmood","family":"Fathy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3694-206X","authenticated-orcid":false,"given":"Guoying","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0579-7763","authenticated-orcid":false,"given":"Ehsan","family":"Adeli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref72","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6247917"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126397"},{"key":"ref39","first-page":"2496","article-title":"Robust PCA via outlier pursuit","author":"xu","year":"2010","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref38","article-title":"Coherence pursuit: Fast, simple, and robust principal component analysis","author":"rahmani","year":"2016","journal-title":"arXiv 1609 04789"},{"key":"ref33","first-page":"663","article-title":"Robust subspace segmentation by low-rank representation","author":"liu","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn (ICML)"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2015.7301284"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.64"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2011.09.009"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1023\/B:DAMI.0000023676.72185.7c"},{"key":"ref36","first-page":"1","article-title":"Anomaly detection over noisy data using learned probability distributions","author":"eskin","year":"2000","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00810"},{"key":"ref34","first-page":"658","article-title":"Robust feature-sample linear discriminant analysis for brain disorders diagnosis","author":"adeli","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref60","article-title":"TensorFlow: Large-scale machine learning on heterogeneous distributed systems","author":"abadi","year":"2016","journal-title":"arXiv 1603 04467"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.439"},{"key":"ref61","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1016\/j.sigpro.2003.07.018","article-title":"Novelty detection: A review—Part 1: Statistical approaches","volume":"83","author":"markou","year":"2003","journal-title":"Signal Process"},{"key":"ref64","article-title":"Caltech-256 object category dataset","author":"griffin","year":"2007"},{"key":"ref27","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335388"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2015.114"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1049\/el.2016.0440"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2007.70738"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00114"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00356"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2017.8296547"},{"key":"ref21","article-title":"Learning deep representations of appearance and motion for anomalous event detection","author":"xu","year":"2015","journal-title":"arXiv 1510 01553"},{"key":"ref24","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":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.68"},{"key":"ref26","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s00138-017-0869-8"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2017.145"},{"key":"ref51","article-title":"Global guarantees for enforcing deep generative priors by empirical risk","author":"hand","year":"2017","journal-title":"arXiv 1705 07576"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5273-4"},{"key":"ref58","author":"bishop","year":"2006","journal-title":"Pattern Recognition and Machine Learning"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04898-2_327"},{"key":"ref56","article-title":"Mode regularized generative adversarial networks","author":"che","year":"2016","journal-title":"arXiv 1612 02136"},{"key":"ref55","first-page":"3308","article-title":"Veegan: Reducing mode collapse in gans using implicit variational learning","author":"srivastava","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref54","article-title":"MGGAN: Solving mode collapse using manifold guided training","author":"bang","year":"2018","journal-title":"arXiv 1804 04391"},{"key":"ref53","first-page":"1558","article-title":"Autoencoding beyond pixels using a learned similarity metric","author":"larsen","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3185996"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2011.5995434"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/TPAMI.2013.111","article-title":"Anomaly detection and localization in crowded scenes","volume":"36","author":"li","year":"2014","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1007\/s10208-014-9221-0"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2670780"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2855699"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(95)00120-4"},{"key":"ref15","first-page":"1025","article-title":"One-class novelty detection for seizure analysis from intracranial EEG","volume":"7","author":"gardner","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1017\/S026988891300043X"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2785792"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2015.2418332"},{"key":"ref19","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2320293"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2676110"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.460"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.177"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2329534"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2016.2526063"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.38"},{"key":"ref46","article-title":"Image-to-image translation with conditional adversarial networks","author":"isola","year":"2016","journal-title":"ArXiv 1611 07004"},{"key":"ref45","article-title":"Training adversarial discriminators for cross-channel abnormal event detection in crowds","author":"ravanbakhsh","year":"2017","journal-title":"arXiv 1706 07680"},{"key":"ref48","first-page":"488","article-title":"Avid: Adversarial visual irregularity detection","author":"sabokrou","year":"2018","journal-title":"Proc Asian Comput Vis Conf"},{"key":"ref47","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Comput -Assist Intervent"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2011.6126525"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-006-0009-9"},{"key":"ref44","first-page":"146","article-title":"Unsupervised anomaly detection with generative adversarial networks to guide marker discovery","author":"schlegl","year":"2017","journal-title":"Proc Int Conf Inf Process Med Imag"},{"key":"ref43","first-page":"2234","article-title":"Improved techniques for training gans","author":"salimans","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"}],"container-title":["IEEE Transactions on Neural Networks and Learning Systems"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/5962385\/9346113\/09059022.pdf?arnumber=9059022","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:53:10Z","timestamp":1652194390000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9059022\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2]]},"references-count":72,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.1109\/tnnls.2020.2979049","relation":{},"ISSN":["2162-237X","2162-2388"],"issn-type":[{"value":"2162-237X","type":"print"},{"value":"2162-2388","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2]]}}}