{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T20:33:01Z","timestamp":1771705981048,"version":"3.50.1"},"reference-count":57,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MF136"],"award-info":[{"award-number":["ZR2020MF136"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2022LZH015"],"award-info":[{"award-number":["ZR2022LZH015"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2020MF006"],"award-info":[{"award-number":["ZR2020MF006"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2023LZH017"],"award-info":[{"award-number":["ZR2023LZH017"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University","award":["BTBD-2020KF05"],"award-info":[{"award-number":["BTBD-2020KF05"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["22CX01001A-1"],"award-info":[{"award-number":["22CX01001A-1"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["22CX01001A-3"],"award-info":[{"award-number":["22CX01001A-3"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Industry-University Research Innovation Foundation of Ministry of Education of China","award":["2021FNA01001"],"award-info":[{"award-number":["2021FNA01001"]}]},{"name":"Open Foundation of State Key Laboratory of Integrated Services Networks","award":["ISN23-09"],"award-info":[{"award-number":["ISN23-09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Consumer Electron."],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1109\/tce.2023.3319131","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T17:47:56Z","timestamp":1695750476000},"page":"1952-1962","source":"Crossref","is-referenced-by-count":10,"title":["Anomaly Detection With Memory-Augmented Adversarial Autoencoder Networks for Industry 5.0"],"prefix":"10.1109","volume":"70","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6914-043X","authenticated-orcid":false,"given":"Huan","family":"Zhang","sequence":"first","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3020-3947","authenticated-orcid":false,"given":"Neeraj","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology (Deemed to be University), Patiala, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9947-9968","authenticated-orcid":false,"given":"Sheng","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0944-2564","authenticated-orcid":false,"given":"Chunlei","family":"Wu","sequence":"additional","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4316-932X","authenticated-orcid":false,"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Science, China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0990-5581","authenticated-orcid":false,"given":"Peiying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"ref2","article-title":"Attribute restoration framework for anomaly detection","author":"Huang","year":"2019","journal-title":"arXiv:1911.10676"},{"key":"ref3","article-title":"Normality-calibrated autoencoder for unsupervised anomaly detection on data contamination","author":"Yu","year":"2021","journal-title":"arXiv:2110.14825"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2019.00179"},{"key":"ref5","article-title":"Adversarial autoencoders","author":"Makhzani","year":"2015","journal-title":"arXiv:1511.05644"},{"key":"ref6","volume-title":"Avtpnet: Convolutional autoencoder for AVTP anomaly detection in automotive ethernet networks","author":"Alkhatib","year":"2022"},{"key":"ref7","first-page":"1","article-title":"Deep autoencoding Gaussian mixture model for unsupervised anomaly detection","volume-title":"Proc. 6th Int. Conf. Learn. Represent. (ICLR)","author":"Zong"},{"key":"ref8","first-page":"1","article-title":"Generative adversarial nets","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"27","author":"Goodfellow"},{"key":"ref9","first-page":"3379","article-title":"Adversarially learned one-class classifier for novelty detection","volume-title":"Proc. IEEE Conf. Comput. Vis. Pattern Recognit.","author":"Sabokrou"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ijcnn.2019.8851808"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref14","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.90"},{"key":"ref16","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"Radford","year":"2015","journal-title":"arXiv:1511.06434"},{"key":"ref17","volume-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"ref18","volume-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"ref19","volume-title":"On the relationship between self-attention and convolutional layers","author":"Cordonnier","year":"2019"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2018.2864142"},{"key":"ref21","first-page":"24261","article-title":"MLP-mixer: An all-MLP architecture for vision","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Tolstikhin"},{"key":"ref22","volume-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/iccv48922.2021.00009"},{"key":"ref24","volume-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"key":"ref25","volume-title":"Convtransformer: A convolutional transformer network for video frame synthesis","author":"Liu","year":"2020"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2009.5425504"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1002\/wics.19"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-015-0341-4"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1145\/2689746.2689747"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098052"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-45823-6_67"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.03.004"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2021.04.089"},{"issue":"1","key":"ref34","first-page":"1","article-title":"Variational autoencoder based anomaly detection using reconstruction probability","volume":"2","author":"An","year":"2015","journal-title":"Special Lecture IE"},{"key":"ref35","first-page":"14360","article-title":"Learning memory-guided normality for anomaly detection","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","author":"Noh"},{"key":"ref36","first-page":"14171","article-title":"Old is gold: Redefining the adversarially learned one-class classifier training paradigm","volume-title":"Proc. IEEE\/CVF Conf. Comput. Vis. Pattern Recognit.","author":"Zaheer"},{"key":"ref37","article-title":"Unsupervised dual adversarial learning for anomaly detection in colonoscopy video frames","author":"Liu","year":"2019","journal-title":"arXiv:1910.10345"},{"key":"ref38","volume-title":"DOPING: generative data augmentation for unsupervised anomaly detection with GAN","author":"Lim","year":"2018"},{"key":"ref39","volume-title":"Self-attention generative adversarial networks","author":"Zhang","year":"2018"},{"key":"ref40","volume-title":"Efficient GAN-based anomaly detection","author":"Zenati","year":"2018"},{"key":"ref41","volume-title":"PaDiM: A patch distribution modeling framework for anomaly detection and localization","author":"Defard","year":"2020"},{"key":"ref42","volume-title":"Student-teacher feature pyramid matching for unsupervised anomaly detection","author":"Wang","year":"2021"},{"key":"ref43","volume-title":"Pyramid vision transformer: A versatile backbone for dense prediction without convolutions","author":"Wang","year":"2021"},{"key":"ref44","first-page":"10347","article-title":"Training data-efficient image transformers & distillation through attention","volume-title":"Proc. 38th Int. Conf. Mach. Learn.","author":"Touvron"},{"key":"ref45","first-page":"9355","article-title":"Twins: Revisiting the design of spatial attention in vision transformers","volume-title":"Proc. Adv. Neural Inf. Process. Syst. 34th Annu. Conf. Neural Inf. Process. Syst.","author":"Chu"},{"key":"ref46","volume-title":"Rest: An efficient transformer for visual recognition","author":"Zhang","year":"2021"},{"key":"ref47","first-page":"3965","article-title":"CoAtNet: Marrying convolution and attention for all data sizes","volume":"34","author":"Dai","year":"2021","journal-title":"Advances in neural information processing systems"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01186"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00042"},{"issue":"4","key":"ref50","first-page":"1","article-title":"Learning multiple layers of features from tiny images","volume":"1","author":"Krizhevsky","year":"2009","journal-title":"Handb. Syst. Autoimmune Dis."},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00982"},{"key":"ref53","first-page":"215","article-title":"An analysis of single-layer networks in unsupervised feature learning","volume-title":"Proc. 14th Int. Conf. Artif. Intell. Statist.","author":"Coates"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.07.009"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1023\/B:MACH.0000008084.60811.49"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/icip46576.2022.9897283"}],"container-title":["IEEE Transactions on Consumer Electronics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/30\/10510045\/10263623.pdf?arnumber=10263623","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,12]],"date-time":"2025-02-12T18:22:47Z","timestamp":1739384567000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10263623\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":57,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tce.2023.3319131","relation":{},"ISSN":["0098-3063","1558-4127"],"issn-type":[{"value":"0098-3063","type":"print"},{"value":"1558-4127","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]}}}