{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T00:15:16Z","timestamp":1778285716302,"version":"3.51.4"},"reference-count":39,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003662","name":"Korea Evaluation Institute of Industrial Technology","doi-asserted-by":"publisher","award":["RS-2024-00442354"],"award-info":[{"award-number":["RS-2024-00442354"]}],"id":[{"id":"10.13039\/501100003662","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114744","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T21:35:11Z","timestamp":1775684111000},"page":"114744","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"P1","title":["Joint reconstruction and localization network via pre-trained feature mapping for industrial anomaly detection"],"prefix":"10.1016","volume":"176","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9732-8648","authenticated-orcid":false,"given":"Yunhan","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114744_bib1","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1007\/s11263-020-01400-4","article-title":"The MVTec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection","volume":"129","author":"Bergmann","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.engappai.2026.114744_bib2","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"3606","article-title":"Describing textures in the wild","author":"Cimpoi","year":"2014"},{"key":"10.1016\/j.engappai.2026.114744_bib3","article-title":"Sub-image anomaly detection with deep pyramid correspondences","author":"Cohen","year":"2020","journal-title":"arXiv preprint arXiv:2005.02357"},{"key":"10.1016\/j.engappai.2026.114744_bib4","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops","first-page":"702","article-title":"Randaugment: practical automated data augmentation with a reduced search space","author":"Cubuk","year":"2020"},{"key":"10.1016\/j.engappai.2026.114744_bib5","series-title":"International Conference on Pattern Recognition","first-page":"475","article-title":"Padim: a patch distribution modeling framework for anomaly detection and localization","author":"Defard","year":"2021"},{"key":"10.1016\/j.engappai.2026.114744_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110006","article-title":"Inspection of cracking in stamping parts surfaces using anomaly detection","volume":"143","author":"Dong","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114744_bib7","doi-asserted-by":"crossref","DOI":"10.1016\/j.optlaseng.2024.108457","article-title":"Efficient visual anomaly detection model with adaptive wavelet transform","volume":"182","author":"Du","year":"2024","journal-title":"Opt Laser. Eng."},{"key":"10.1016\/j.engappai.2026.114744_bib8","doi-asserted-by":"crossref","DOI":"10.1016\/j.optlastec.2023.110296","article-title":"Anomaly-prior guided inpainting for industrial visual anomaly detection","volume":"170","author":"Du","year":"2024","journal-title":"Opt. Laser Technol."},{"key":"10.1016\/j.engappai.2026.114744_bib9","series-title":"European Conference on Computer Vision","first-page":"91","article-title":"Transfusion\u2013a transparency-based diffusion model for anomaly detection","author":"Fu\u010dka","year":"2024"},{"key":"10.1016\/j.engappai.2026.114744_bib10","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"8472","article-title":"A diffusion-based framework for multi-class anomaly detection","author":"He","year":"2024"},{"key":"10.1016\/j.engappai.2026.114744_bib11","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"19606","article-title":"Winclip: Zero-\/few-shot anomaly classification and segmentation","author":"Jeong","year":"2023"},{"key":"10.1016\/j.engappai.2026.114744_bib12","article-title":"Anomaly detection by effectively leveraging synthetic images","author":"Kang","year":"2025","journal-title":"arXiv preprint arXiv:2512.23227"},{"key":"10.1016\/j.engappai.2026.114744_bib13","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1007\/s10845-021-01764-5","article-title":"Machining quality monitoring (MQM) in laser-assisted micro-milling of glass using cutting force signals: an image-based deep transfer learning","volume":"33","author":"Kim","year":"2022","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.engappai.2026.114744_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2019.106544","article-title":"Phase-based time domain averaging (PTDA) for fault detection of a gearbox in an industrial robot using vibration signals","volume":"138","author":"Kim","year":"2020","journal-title":"Mech. Syst. Signal Process."},{"key":"10.1016\/j.engappai.2026.114744_bib15","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.jmsy.2024.12.005","article-title":"Reconstruction-based visual anomaly detection in wound rotor synchronous machine production using convolutional autoencoders and structural similarity","volume":"78","author":"Kohler","year":"2025","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.engappai.2026.114744_bib16","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2024.104198","article-title":"A novel FuseDecode Autoencoder for industrial visual inspection: incremental anomaly detection improvement with gradual transition from unsupervised to mixed-supervision learning with reduced human effort","volume":"164","author":"Kozamernik","year":"2025","journal-title":"Comput. Ind."},{"key":"10.1016\/j.engappai.2026.114744_bib17","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1007\/s11633-023-1459-z","article-title":"Deep industrial image anomaly detection: a survey","volume":"21","author":"Liu","year":"2024","journal-title":"Mach. Intell. Res."},{"key":"10.1016\/j.engappai.2026.114744_bib18","article-title":"Part quality inspection image data (car) dataset (ID: 578)","year":"2021","journal-title":"AI-Hub"},{"key":"10.1016\/j.engappai.2026.114744_bib19","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1145\/325165.325247","article-title":"An image synthesizer","volume":"19","author":"Perlin","year":"1985","journal-title":"ACM Siggraph Comput. Graph."},{"key":"10.1016\/j.engappai.2026.114744_bib20","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","volume":"vol. 18","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.engappai.2026.114744_bib21","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"14318","article-title":"Towards total recall in industrial anomaly detection","author":"Roth","year":"2022"},{"key":"10.1016\/j.engappai.2026.114744_bib22","doi-asserted-by":"crossref","first-page":"5605","DOI":"10.1109\/TKDE.2024.3404027","article-title":"Anomaly detection under contaminated data with contamination-immune bidirectional gans","volume":"36","author":"Su","year":"2024","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.engappai.2026.114744_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112364","article-title":"MTDiff: visual anomaly detection with multi-scale diffusion models","volume":"302","author":"Wang","year":"2024","journal-title":"Knowl. Base Syst."},{"key":"10.1016\/j.engappai.2026.114744_bib24","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2026.114744_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2024.104192","article-title":"TDAD: Self-supervised industrial anomaly detection with a two-stage diffusion model","volume":"164","author":"Wei","year":"2025","journal-title":"Comput. Ind."},{"key":"10.1016\/j.engappai.2026.114744_bib26","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.jmsy.2024.02.001","article-title":"AEKD: unsupervised auto-encoder knowledge distillation for industrial anomaly detection","volume":"73","author":"Wu","year":"2024","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.engappai.2026.114744_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107369","article-title":"Unsupervised anomaly detection in images using attentional normalizing flows","volume":"127","author":"Wu","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114744_bib28","series-title":"European Conference on Computer Vision","first-page":"1","article-title":"Glad: towards better reconstruction with global and local adaptive diffusion models for unsupervised anomaly detection","author":"Yao","year":"2024"},{"key":"10.1016\/j.engappai.2026.114744_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109235","article-title":"Local\u2013global normality learning and discrepancy normalizing flow for unsupervised image anomaly detection","volume":"137","author":"Yao","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114744_bib30","article-title":"Dual-student knowledge distillation networks for unsupervised anomaly detection","author":"Yao","year":"2024","journal-title":"arXiv preprint arXiv:2402.00448"},{"key":"10.1016\/j.engappai.2026.114744_bib31","article-title":"Fastflow: unsupervised anomaly detection and localization via 2d normalizing flows","author":"Yu","year":"2021","journal-title":"arXiv preprint arXiv:2111.07677"},{"key":"10.1016\/j.engappai.2026.114744_bib32","article-title":"Wide residual networks","author":"Zagoruyko","year":"2016","journal-title":"arXiv preprint arXiv:1605.07146"},{"key":"10.1016\/j.engappai.2026.114744_bib33","series-title":"European Conference on Computer Vision","first-page":"539","article-title":"Dsr\u2013a dual subspace re-projection network for surface anomaly detection","author":"Zavrtanik","year":"2022"},{"key":"10.1016\/j.engappai.2026.114744_bib34","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"8330","article-title":"Draem-a discriminatively trained reconstruction embedding for surface anomaly detection","author":"Zavrtanik","year":"2021"},{"key":"10.1016\/j.engappai.2026.114744_bib35","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107706","article-title":"Reconstruction by inpainting for visual anomaly detection","volume":"112","author":"Zavrtanik","year":"2021","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.engappai.2026.114744_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110866","article-title":"Cloud-GAN: Cloud generation adversarial networks for anomaly detection","volume":"157","author":"Zeng","year":"2025","journal-title":"Pattern Recogn."},{"key":"10.1016\/j.engappai.2026.114744_bib37","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"16699","article-title":"RealNet: a feature selection network with realistic synthetic anomaly for anomaly detection","author":"Zhang","year":"2024"},{"key":"10.1016\/j.engappai.2026.114744_bib38","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109852","article-title":"Frequency domain nuances guided parallel transformer model for industrial anomaly localization","volume":"142","author":"Zhao","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114744_bib39","series-title":"European Conference on Computer Vision","first-page":"392","article-title":"Spot-the-difference self-supervised pre-training for anomaly detection and segmentation","author":"Zou","year":"2022"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010262?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626010262?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T23:36:12Z","timestamp":1778283372000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626010262"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":39,"alternative-id":["S0952197626010262"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114744","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Joint reconstruction and localization network via pre-trained feature mapping for industrial anomaly detection","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114744","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114744"}}