{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T06:04:45Z","timestamp":1771049085686,"version":"3.50.1"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100014188","name":"Ministry of Science and ICT, South Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003665","name":"National IT Industry Promotion Agency","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,1]]},"DOI":"10.1007\/s00521-025-11775-5","type":"journal-article","created":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T05:54:11Z","timestamp":1769493251000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Synthesis-guided unsupervised anomaly detection in industrial images with large language model-driven analysis"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3905-9774","authenticated-orcid":false,"given":"Asim","family":"Niaz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9555-0309","authenticated-orcid":false,"given":"Muhammad","family":"Umraiz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Syed Farhan","family":"Alam Zaidi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Farhan","family":"Akram","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7420-9216","authenticated-orcid":false,"given":"Kwang Nam","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,27]]},"reference":[{"issue":"1","key":"11775_CR1","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s13755-023-00221-2","volume":"11","author":"D Samariya","year":"2023","unstructured":"Samariya D, Ma J, Aryal S, Zhao X (2023) Detection and explanation of anomalies in healthcare data. Health Information Science and Systems 11(1):20","journal-title":"Health Information Science and Systems"},{"issue":"18","key":"11775_CR2","doi-asserted-by":"publisher","first-page":"3007","DOI":"10.3390\/diagnostics13183007","volume":"13","author":"R Kaifi","year":"2023","unstructured":"Kaifi R (2023) A review of recent advances in brain tumor diagnosis based on ai-based classification. Diagnostics 13(18):3007","journal-title":"Diagnostics"},{"key":"11775_CR3","unstructured":"Huang W, Wei P (2019) A pcb dataset for defects detection and classification arXiv preprint arXiv:1901.08204"},{"key":"11775_CR4","doi-asserted-by":"crossref","unstructured":"Liao S, Huang C, Zhang H, Gong J, Li M, Wang Z (2022). Object detection of welding defects in smt electronics production based on deep learning. In: 2022 23rd International Conference on Electronic Packaging Technology (ICEPT). pp 1\u20135 IEEE","DOI":"10.1109\/ICEPT56209.2022.9873297"},{"key":"11775_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109161","volume":"137","author":"S Zhou","year":"2024","unstructured":"Zhou S, Cheng S, Zhang D, Wang Z, Zhang S, Zhu Y, Wang H (2024) Steering knuckle surface defect detection and segmentation based on reverse residual distillation. Eng Appl Artif Intell 137:109161","journal-title":"Eng Appl Artif Intell"},{"key":"11775_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107369","volume":"127","author":"X Wu","year":"2024","unstructured":"Wu X, Mao G, Xing S (2024) Unsupervised anomaly detection in images using attentional normalizing flows. Eng Appl Artif Intell 127:107369","journal-title":"Eng Appl Artif Intell"},{"key":"11775_CR7","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.future.2021.01.033","volume":"119","author":"L Rosa","year":"2021","unstructured":"Rosa L, Cruz T, De Freitas MB, Quit\u00e9rio P, Henriques J, Caldeira F, Monteiro E, Sim\u00f5es P (2021) Intrusion and anomaly detection for the next-generation of industrial automation and control systems. Futur Gener Comput Syst 119:50\u201367","journal-title":"Futur Gener Comput Syst"},{"key":"11775_CR8","doi-asserted-by":"crossref","unstructured":"Sharma A, Sharma A, Tselykh A, Bozhenyuk A, Kim B-G (2024) Image and video analysis using graph neural network for internet of medical things and computer vision applications. CAAI Transactions on Intelligence Technology","DOI":"10.1049\/cit2.12306"},{"issue":"25","key":"11775_CR9","doi-asserted-by":"publisher","first-page":"15799","DOI":"10.1007\/s00521-024-09911-8","volume":"36","author":"B Xi","year":"2024","unstructured":"Xi B, Chen Q (2024) Real-time anomaly detection for \u2018remote\u2019bus stop surveillance using unsupervised conditional generative adversarial networks. Neural Comput Appl 36(25):15799\u201315813","journal-title":"Neural Comput Appl"},{"issue":"34","key":"11775_CR10","doi-asserted-by":"publisher","first-page":"21561","DOI":"10.1007\/s00521-024-10291-2","volume":"36","author":"V \u0160kv\u00e1ra","year":"2024","unstructured":"\u0160kv\u00e1ra V, \u0160m\u00eddl V, Pevn\u1ef3 T (2024) Anomaly detection in multifactor data. Neural Comput Appl 36(34):21561\u201321580","journal-title":"Neural Comput Appl"},{"issue":"29","key":"11775_CR11","doi-asserted-by":"publisher","first-page":"18499","DOI":"10.1007\/s00521-024-10172-8","volume":"36","author":"X Wang","year":"2024","unstructured":"Wang X, Wang Y, Pan Z, Wang G (2024) Unsupervised anomaly detection and localization via bidirectional knowledge distillation. Neural Comput Appl 36(29):18499\u201318514","journal-title":"Neural Comput Appl"},{"issue":"4","key":"11775_CR12","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1049\/cit2.12116","volume":"8","author":"Z Zhao","year":"2023","unstructured":"Zhao Z, Sun B (2023) Hyperspectral anomaly detection via memory-augmented autoencoders. CAAI Transactions on Intelligence Technology 8(4):1274\u20131287","journal-title":"CAAI Transactions on Intelligence Technology"},{"issue":"3","key":"11775_CR13","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1049\/cit2.12068","volume":"7","author":"X Zheng","year":"2022","unstructured":"Zheng X, Zhang Y, Zheng Y, Luo F, Lu X (2022) Abnormal event detection by a weakly supervised temporal attention network. CAAI Transactions on Intelligence Technology 7(3):419\u2013431","journal-title":"CAAI Transactions on Intelligence Technology"},{"key":"11775_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107810","volume":"99","author":"T Saba","year":"2022","unstructured":"Saba T, Rehman A, Sadad T, Kolivand H, Bahaj SA (2022) Anomaly-based intrusion detection system for iot networks through deep learning model. Comput Electr Eng 99:107810","journal-title":"Comput Electr Eng"},{"key":"11775_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108470","volume":"106","author":"MA Contreras-Cruz","year":"2023","unstructured":"Contreras-Cruz MA, Correa-Tome FE, Lopez-Padilla R, Ramirez-Paredes J-P (2023) Generative adversarial networks for anomaly detection in aerial images. Comput Electr Eng 106:108470","journal-title":"Comput Electr Eng"},{"key":"11775_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109759","volume":"120","author":"X Sun","year":"2024","unstructured":"Sun X, Pan W, Qin J, Lang Y, Qian Y (2024) Unsupervised industry anomaly detection via asymmetric reverse distillation. Comput Electr Eng 120:109759","journal-title":"Comput Electr Eng"},{"key":"11775_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2024.103958","volume":"241","author":"E Schwartz","year":"2024","unstructured":"Schwartz E, Arbelle A, Karlinsky L, Harary S, Scheidegger F, Doveh S, Giryes R (2024) Maeday: Mae for few-and zero-shot anomaly-detection. Comput Vis Image Underst 241:103958","journal-title":"Comput Vis Image Underst"},{"key":"11775_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2025.104308","volume":"253","author":"J Zhang","year":"2025","unstructured":"Zhang J, Chen X, Wang Y, Wang C, Liu Y, Li X, Yang M-H, Tao D (2025) Exploring plain vit features for multi-class unsupervised visual anomaly detection. Computer Vision and Image Understanding 253:104308","journal-title":"Computer Vision and Image Understanding"},{"key":"11775_CR19","doi-asserted-by":"crossref","unstructured":"Niaz A, Amin SU, Soomro S, Zia H, Choi KN (2024) Spatially aware fusion in 3d convolutional autoencoders for video anomaly detection. IEEE Access","DOI":"10.1109\/ACCESS.2024.3435144"},{"key":"11775_CR20","doi-asserted-by":"publisher","first-page":"4426","DOI":"10.1109\/TMM.2022.3175611","volume":"25","author":"C Huang","year":"2022","unstructured":"Huang C, Xu Q, Wang Y, Wang Y, Zhang Y (2022) Self-supervised masking for unsupervised anomaly detection and localization. IEEE Transactions on Multimedia 25:4426\u20134438","journal-title":"IEEE Transactions on Multimedia"},{"key":"11775_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2023.103901","volume":"148","author":"X Shi","year":"2023","unstructured":"Shi X, Zhang S, Cheng M, He L, Tang X, Cui Z (2023) Few-shot semantic segmentation for industrial defect recognition. Comput Ind 148:103901","journal-title":"Comput Ind"},{"issue":"1","key":"11775_CR22","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/s11633-023-1459-z","volume":"21","author":"J Liu","year":"2024","unstructured":"Liu J, Xie G, Wang J, Li S, Wang C, Zheng F, Jin Y (2024) Deep industrial image anomaly detection: A survey. Machine Intelligence Research 21(1):104\u2013135","journal-title":"Machine Intelligence Research"},{"key":"11775_CR23","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1109\/TMM.2020.3046884","volume":"24","author":"F Ye","year":"2020","unstructured":"Ye F, Huang C, Cao J, Li M, Zhang Y, Lu C (2020) Attribute restoration framework for anomaly detection. IEEE Trans Multimedia 24:116\u2013127","journal-title":"IEEE Trans Multimedia"},{"key":"11775_CR24","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Advances in neural information processing systems 27"},{"key":"11775_CR25","doi-asserted-by":"crossref","unstructured":"Gong D, Liu L, Le V, Saha B, Mansour MR, Venkatesh S, Hengel Avd (2019) Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. pp 1705\u20131714","DOI":"10.1109\/ICCV.2019.00179"},{"key":"11775_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107706","volume":"112","author":"V Zavrtanik","year":"2021","unstructured":"Zavrtanik V, Kristan M, Sko\u010daj D (2021) Reconstruction by inpainting for visual anomaly detection. Pattern Recogn 112:107706","journal-title":"Pattern Recogn"},{"key":"11775_CR27","doi-asserted-by":"crossref","unstructured":"Rudolph M, Wehrbein T, Rosenhahn B, Wandt B (2022) Fully convolutional cross-scale-flows for image-based defect detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 1088\u20131097","DOI":"10.1109\/WACV51458.2022.00189"},{"key":"11775_CR28","doi-asserted-by":"crossref","unstructured":"Roth K, Pemula L, Zepeda J, Sch\u00f6lkopf B, Brox T, Gehler P (2022) Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition pp 14318\u201314328","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"11775_CR29","doi-asserted-by":"crossref","unstructured":"Deng H, Li X (2022) Anomaly detection via reverse distillation from one-class embedding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 9737\u20139746","DOI":"10.1109\/CVPR52688.2022.00951"},{"key":"11775_CR30","doi-asserted-by":"crossref","unstructured":"Defard T, Setkov A, Loesch A, Audigier R (2021). Padim: a patch distribution modeling framework for anomaly detection and localization. Springer, In: International Conference on Pattern Recognition pp 475\u2013489","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"11775_CR31","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M-A, Lacroix T, Rozi\u00e8re B, Goyal N, Hambro E, Azhar F et al (2023) Llama: Open and efficient foundation language models arXiv preprint arXiv:2302.13971"},{"key":"11775_CR32","unstructured":"Zhu D, Chen J, Shen X, Li X, Elhoseiny M (2023) Minigpt-4: Enhancing vision-language understanding with advanced large language models. arXiv preprint arXiv:2304.10592"},{"key":"11775_CR33","unstructured":"Li J, Li D, Savarese S, Hoi S (2023) Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. PMLR, In: International Conference on Machine Learning pp 19730\u201319742"},{"key":"11775_CR34","unstructured":"Su Y, Lan T, Li H, Xu J, Wang Y, Cai D (2023) Pandagpt: One model to instruction-follow them all. arXiv preprint arXiv:2305.16355"},{"key":"11775_CR35","unstructured":"OpenAI: ChatGPT API. https:\/\/openai.com\/chatgpt. Accessed: Month, Day, Year (2024)"},{"key":"11775_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2021.101272","volume":"48","author":"D-M Tsai","year":"2021","unstructured":"Tsai D-M, Jen P-H (2021) Autoencoder-based anomaly detection for surface defect inspection. Adv Eng Inform 48:101272","journal-title":"Adv Eng Inform"},{"key":"11775_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2020.101105","volume":"45","author":"JK Chow","year":"2020","unstructured":"Chow JK, Su Z, Wu J, Tan PS, Mao X, Wang Y-H (2020) Anomaly detection of defects on concrete structures with the convolutional autoencoder. Adv Eng Inform 45:101105","journal-title":"Adv Eng Inform"},{"key":"11775_CR38","doi-asserted-by":"crossref","unstructured":"Zimmerer D, Isensee F, Petersen J, Kohl S, Maier-Hein K (2019)Unsupervised anomaly localization using variational auto-encoders. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part IV 22, pp. 289\u2013297. Springer","DOI":"10.1007\/978-3-030-32251-9_32"},{"key":"11775_CR39","first-page":"1","volume":"70","author":"D Zhang","year":"2020","unstructured":"Zhang D, Gao S, Yu L, Kang G, Wei X, Zhan D (2020) Defgan: Defect detection gans with latent space pitting for high-speed railway insulator. IEEE Trans Instrum Meas 70:1\u201310","journal-title":"IEEE Trans Instrum Meas"},{"key":"11775_CR40","doi-asserted-by":"crossref","unstructured":"Rudolph M, Wehrbein T, Rosenhahn B, Wandt B (2023) Asymmetric student-teacher networks for industrial anomaly detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision pp 2592\u20132602","DOI":"10.1109\/WACV56688.2023.00262"},{"key":"11775_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110982","volume":"280","author":"Y Jiang","year":"2023","unstructured":"Jiang Y, Cao Y, Shen W (2023) A masked reverse knowledge distillation method incorporating global and local information for image anomaly detection. Knowl-Based Syst 280:110982","journal-title":"Knowl-Based Syst"},{"key":"11775_CR42","doi-asserted-by":"crossref","unstructured":"Yan X, Zhang H, Xu X, Hu X, Heng P-A (2021) Learning semantic context from normal samples for unsupervised anomaly detection. Proceedings of the AAAI Conference on Artificial Intelligence 35:3110\u20133118","DOI":"10.1609\/aaai.v35i4.16420"},{"key":"11775_CR43","doi-asserted-by":"crossref","unstructured":"Li Z, Li N, Jiang K, Ma Z, Wei X, Hong X, Gong Y (2020) Superpixel masking and inpainting for self-supervised anomaly detection. In: Bmvc","DOI":"10.5244\/C.34.77"},{"key":"11775_CR44","doi-asserted-by":"crossref","unstructured":"Bergmann P, L\u00f6we S, Fauser M, Sattlegger D, Steger C (2019)Improving unsupervised defect segmentation by applying structural similarity to autoencoders. In: Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019, Volume 5: VISAPP, Prague, Czech Republic, February 25-27, 2019, pp. 372\u2013380. SciTePress","DOI":"10.5220\/0007364500002108"},{"key":"11775_CR45","unstructured":"Bergman L, Cohen N, Hoshen Y (2020) Deep nearest neighbor anomaly detection. arXiv preprint arXiv:2002.10445"},{"issue":"1","key":"11775_CR46","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3390\/s18010209","volume":"18","author":"P Napoletano","year":"2018","unstructured":"Napoletano P, Piccoli F, Schettini R (2018) Anomaly detection in nanofibrous materials by cnn-based self-similarity. Sensors 18(1):209","journal-title":"Sensors"},{"key":"11775_CR47","unstructured":"Cohen N, Hoshen Y (2020) Sub-image anomaly detection with deep pyramid correspondences. arXiv:2005.02357"},{"key":"11775_CR48","doi-asserted-by":"crossref","unstructured":"Zagoruyko S, Komodakis N (2016) Wide residual networks. In: Proceedings of the British Machine Vision Conference (BMVC), BMVA Press, ???, pp 87\u201318712","DOI":"10.5244\/C.30.87"},{"key":"11775_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2024.102759","volume":"62","author":"Q Liu","year":"2024","unstructured":"Liu Q, He D, Shen Y, Lao Z, Ma R, Li J (2024) Surface defect detection of stay cable sheath based on autoencoder and auxiliary anomaly location. Adv Eng Inform 62:102759","journal-title":"Adv Eng Inform"},{"key":"11775_CR50","doi-asserted-by":"crossref","unstructured":"Gudovskiy D, Ishizaka S, Kozuka K (2022) Cflow-ad: Real-time unsupervised anomaly detection with localization via conditional normalizing flows. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 98\u2013107","DOI":"10.1109\/WACV51458.2022.00188"},{"key":"11775_CR51","doi-asserted-by":"crossref","unstructured":"Rudolph M, Wandt B, Rosenhahn B (2021) Same same but differnet: Semi-supervised defect detection with normalizing flows. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp 1907\u20131916","DOI":"10.1109\/WACV48630.2021.00195"},{"key":"11775_CR52","doi-asserted-by":"crossref","unstructured":"Jeong J, Zou Y, Kim T, Zhang D, Ravichandran A, Dabeer O (2023) Winclip: Zero-\/few-shot anomaly classification and segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp 19606\u201319616","DOI":"10.1109\/CVPR52729.2023.01878"},{"key":"11775_CR53","unstructured":"Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J et al (2021) Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR"},{"key":"11775_CR54","unstructured":"Chen X, Han Y, Zhang J (2023) April-gan: A zero-\/few-shot anomaly classification and segmentation method for cvpr 2023 vand workshop challenge tracks 1&2. 1st place on zero-shot ad and 4th place on few-shot ad. arXiv preprint arXiv:2305.17382"},{"key":"11775_CR55","unstructured":"Zhou Q, Pang G, Tian Y, He S, Chen J (2023) Anomalyclip: Object-agnostic prompt learning for zero-shot anomaly detection. In: The Twelfth International Conference on Learning Representations"},{"key":"11775_CR56","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.comcom.2023.12.031","volume":"216","author":"Q Zhao","year":"2024","unstructured":"Zhao Q, Wang Y, Lin Y, Yan S, Song W, Wang B, Huang J, Chang Y, Qi L, Zhang W (2024) Mixed noise-guided mutual constraint framework for unsupervised anomaly detection in smart industries. Comput Commun 216:45\u201353","journal-title":"Comput Commun"},{"key":"11775_CR57","doi-asserted-by":"crossref","unstructured":"Venkataramanan S, Peng K-C, Singh RV, Mahalanobis A (2020). Attention guided anomaly localization in images. In: European Conference on Computer Vision, pp 485\u2013503 Springer","DOI":"10.1007\/978-3-030-58520-4_29"},{"key":"11775_CR58","doi-asserted-by":"crossref","unstructured":"Zavrtanik V, Kristan M, Sko\u010daj D (2021) Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 8330\u20138339","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"11775_CR59","doi-asserted-by":"crossref","unstructured":"Bergmann P, Fauser M, Sattlegger D, Steger C (2019) Mvtec ad-a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 9592\u20139600","DOI":"10.1109\/CVPR.2019.00982"},{"issue":"4","key":"11775_CR60","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1007\/s11263-020-01400-4","volume":"129","author":"P Bergmann","year":"2021","unstructured":"Bergmann P, Batzner K, Fauser M, Sattlegger D, Steger C (2021) The mvtec anomaly detection dataset: a comprehensive real-world dataset for unsupervised anomaly detection. Int J Comput Vision 129(4):1038\u20131059","journal-title":"Int J Comput Vision"},{"key":"11775_CR61","doi-asserted-by":"crossref","unstructured":"Akcay S, Atapour-Abarghouei A, Breckon TP (2019) Ganomaly: Semi-supervised anomaly detection via adversarial training. In: Computer Vision\u2013ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2\u20136, 2018, Revised Selected Papers, Part III 14, pp. 622\u2013637. Springer","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"11775_CR62","doi-asserted-by":"crossref","unstructured":"Bergmann P, Fauser M, Sattlegger D, Steger C (2020) Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp 4183\u20134192","DOI":"10.1109\/CVPR42600.2020.00424"},{"issue":"12","key":"11775_CR63","doi-asserted-by":"publisher","first-page":"3336","DOI":"10.3390\/s20123336","volume":"20","author":"T-W Tang","year":"2020","unstructured":"Tang T-W, Kuo W-H, Lan J-H, Ding C-F, Hsu H, Young H-T (2020) Anomaly detection neural network with dual auto-encoders gan and its industrial inspection applications. Sensors 20(12):3336","journal-title":"Sensors"},{"key":"11775_CR64","doi-asserted-by":"crossref","unstructured":"Rippel O, Mertens P, Merhof D (2021). Modeling the distribution of normal data in pre-trained deep features for anomaly detection. In: 2020 25th International Conference on Pattern Recognition (ICPR). pp 6726\u20136733 IEEE","DOI":"10.1109\/ICPR48806.2021.9412109"},{"key":"11775_CR65","doi-asserted-by":"publisher","first-page":"46717","DOI":"10.1109\/ACCESS.2022.3171559","volume":"10","author":"Y Lee","year":"2022","unstructured":"Lee Y, Kang P (2022) Anovit: Unsupervised anomaly detection and localization with vision transformer-based encoder-decoder. IEEE Access 10:46717\u201346724","journal-title":"IEEE Access"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11775-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11775-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11775-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:19:47Z","timestamp":1771046387000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11775-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1]]},"references-count":65,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["11775"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11775-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1]]},"assertion":[{"value":"18 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"21"}}