{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:10:58Z","timestamp":1784542258718,"version":"3.55.0"},"reference-count":30,"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"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Ind. Inf."],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1109\/tii.2022.3182781","type":"journal-article","created":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T20:26:32Z","timestamp":1655238392000},"page":"3208-3218","source":"Crossref","is-referenced-by-count":74,"title":["EID-GAN: Generative Adversarial Nets for Extremely Imbalanced Data Augmentation"],"prefix":"10.1109","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3135-0447","authenticated-orcid":false,"given":"Wei","family":"Li","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangsu Key Laboratory of Media Design and Software Technology, Science Center for Future Foods, Jiangnan University, Wuxi, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3923-8844","authenticated-orcid":false,"given":"Jinlin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2725-2529","authenticated-orcid":false,"given":"Jiannong","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7443-6267","authenticated-orcid":false,"given":"Chao","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Cyber Science and Engineering, Wuhan University, Wuhan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Intelligent Science, Xi’an Jiaotong-Liverpool University, Suzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6079-009X","authenticated-orcid":false,"given":"Xiaohui","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Intelligent Science, Xi’an Jiaotong-Liverpool University, Suzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3789-7686","authenticated-orcid":false,"given":"Ping","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Massachusetts Boston, Boston, MA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1","article-title":"Unsupervised representation learning with deep convolutional GANs","author":"Metz","year":"2015","journal-title":"Comput. Sci."},{"key":"ref2","first-page":"1","article-title":"Towards principled methods for training generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Arjovsky","year":"2017"},{"key":"ref3","first-page":"134","article-title":"Can we eliminate fault escape in self testing by polynomial division (signature analysis)?","volume-title":"Proc. Int. Test Conf. Three Faces Test: Design, Characterization, Prod.","author":"Bhavsar","year":"1984"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Nitesh","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref5","first-page":"1","article-title":"Contrastive syn-to-real generalization","volume-title":"ICLR","author":"Chen","year":"2021"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2017.2759102"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3272127.3275081"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.09.013"},{"key":"ref9","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.3156\/jsoft.29.5_177_2"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/LSP.2009.2024736"},{"key":"ref12","first-page":"5767","article-title":"Improved training of Wasserstein GANs","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Gulrajani","year":"2017"},{"key":"ref13","first-page":"6626","article-title":"GANs trained by a two time-scale update rule converge to a local Nash equilibrium","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Heusel","year":"2017"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref15","first-page":"21357","article-title":"ContraGAN: Contrastive learning for conditional image generation","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Kang","year":"2020"},{"key":"ref16","first-page":"1","article-title":"Auto-encoding variational Bayes","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Kingma","year":"2014"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2021.3078192"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/IIKI.2014.14"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102082"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.07.001"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107646"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1016\/S1048-9843(01)00081-9"},{"key":"ref23","first-page":"1","article-title":"BAGAN: Data augmentation with balancing GAN","author":"Mariani","year":"2018","journal-title":"CoRR"},{"key":"ref24","first-page":"1","article-title":"Spectral normalization for generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Miyato","year":"2018"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-016-9696-4"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2020.3019286"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7502-7_79-1"}],"container-title":["IEEE Transactions on Industrial Informatics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9424\/10064202\/09795891.pdf?arnumber=9795891","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T01:05:07Z","timestamp":1709341507000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9795891\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":30,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tii.2022.3182781","relation":{},"ISSN":["1551-3203","1941-0050"],"issn-type":[{"value":"1551-3203","type":"print"},{"value":"1941-0050","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3]]}}}