{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T21:02:13Z","timestamp":1784408533363,"version":"3.55.0"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819234028","type":"print"},{"value":"9789819234035","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:00:00Z","timestamp":1784419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2027]]},"DOI":"10.1007\/978-981-92-3403-5_36","type":"book-chapter","created":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T20:06:51Z","timestamp":1784405211000},"page":"461-472","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FeatDeNoiseNet: Multi-scale Structure-Consistent Manifold Modeling for Unsupervised Anomaly Localization"],"prefix":"10.1007","author":[{"given":"Hongxiao","family":"Fei","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhubang","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liujie","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yueyi","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"key":"36_CR1","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD\u2014A comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9592\u20139600. (2019)","DOI":"10.1109\/CVPR.2019.00982"},{"key":"36_CR2","unstructured":"Chalapathy, R., Chawla, S.: Deep learning for anomaly detection: a survey. arXiv preprint arXiv:1901.03407. https:\/\/arxiv.org\/abs\/1901.03407 (2019)"},{"issue":"2","key":"36_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(2), 1\u201338 (2021)","journal-title":"ACM Comput. Surv."},{"issue":"5","key":"36_CR4","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1109\/JPROC.2021.3052449","volume":"109","author":"L Ruff","year":"2021","unstructured":"Ruff, L., et al.: A unifying review of deep and shallow anomaly detection. Proc. IEEE. 109(5), 756\u2013795 (2021)","journal-title":"Proc. IEEE"},{"issue":"4","key":"36_CR5","doi-asserted-by":"publisher","first-page":"653","DOI":"10.1016\/j.eng.2019.01.014","volume":"5","author":"F Tao","year":"2019","unstructured":"Tao, F., Qi, Q., Wang, L., Nee, A.Y.C.: digital twins and cyber\u2013physical systems toward smart manufacturing and Industry 4.0: correlation and comparison. Engineering. 5(4), 653\u2013661 (2019)","journal-title":"Engineering"},{"issue":"1","key":"36_CR6","first-page":"23","volume":"4","author":"T Wuest","year":"2016","unstructured":"Wuest, T., Weimer, D., Irgens, C., Thoben, K.D.: Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4(1), 23\u201345 (2016)","journal-title":"Prod. Manuf. Res."},{"key":"36_CR7","unstructured":"Cohen, N., Hoshen, Y.: Sub-image anomaly detection with deep pyramid correspondences. arXiv preprint arXiv:2005.02357 https:\/\/arxiv.org\/abs\/2005.02357 (2020)"},{"key":"36_CR8","first-page":"475","volume-title":"Proceedings of the 25th International Conference on Pattern Recognition (ICPR)","author":"T Defard","year":"2021","unstructured":"Defard, T., Setkov, A., Loesch, A., Audigier, R.: PaDiM: a patch distribution modeling framework for anomaly detection and localization. In: Proceedings of the 25th International Conference on Pattern Recognition (ICPR), pp. 475\u2013489. Springer, Cham (2021)"},{"key":"36_CR9","doi-asserted-by":"crossref","unstructured":"Wang, G., Han, S., Ding, E., Huang, D.: Student-teacher feature pyramid matching for anomaly detection. arXiv preprint arXiv:2103.04257. https:\/\/arxiv.org\/abs\/2103.04257 (2021)","DOI":"10.5244\/C.35.349"},{"key":"36_CR10","unstructured":"Yu, J., et al.: FastFlow: unsupervised anomaly detection and localization via 2D normalizing flows. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8138\u20138147. (2022)"},{"key":"36_CR11","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: DRAEM\u2014a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 8330\u20138339. (2021)","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"36_CR12","doi-asserted-by":"crossref","unstructured":"Roth, K., et al.: Towards total recall in industrial anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14318\u201314328. (2022)","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"36_CR13","doi-asserted-by":"crossref","unstructured":"Gudovskiy, D., Ishizaka, S., Kozuka, K.: 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 (WACV), pp. 98\u2013107. (2022)","DOI":"10.1109\/WACV51458.2022.00188"},{"key":"36_CR14","doi-asserted-by":"crossref","unstructured":"Deng, H., Li, X.: Anomaly detection via reverse distillation from one-class embedding. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9737\u20139746. (2022)","DOI":"10.1109\/CVPR52688.2022.00951"},{"key":"36_CR15","doi-asserted-by":"crossref","unstructured":"Batzner, K., Heckler, L., K\u00f6nig, R.: EfficientAD: accurate visual anomaly detection at millisecond-level latencies. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 128\u2013138. (2024)","DOI":"10.1109\/WACV57701.2024.00020"},{"key":"36_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhou, Y., Xu, Y., Wang, Z.: SimpleNet: a simple network for image anomaly detection and localization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20402\u201320411. (2023)","DOI":"10.1109\/CVPR52729.2023.01954"},{"key":"36_CR17","doi-asserted-by":"crossref","unstructured":"Rudolph, M., Wehrbein, T., Rosenhahn, B., Wandt, B.: Asymmetric student-teacher networks for industrial anomaly detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2592\u20132602. (2023)","DOI":"10.1109\/WACV56688.2023.00262"},{"key":"36_CR18","doi-asserted-by":"crossref","unstructured":"Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multi-resolution knowledge distillation for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14902\u201314912. (2021)","DOI":"10.1109\/CVPR46437.2021.01466"},{"key":"36_CR19","first-page":"622","volume-title":"Proceedings of the Asian Conference on Computer Vision (ACCV)","author":"S Akcay","year":"2018","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Proceedings of the Asian Conference on Computer Vision (ACCV), pp. 622\u2013637. Springer, Cham (2018)"},{"key":"36_CR20","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Uninformed students: student-teacher anomaly detection with discriminative latent embeddings. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4183\u20134192. (2020)","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"36_CR21","doi-asserted-by":"crossref","unstructured":"Guo, J., et al.: Dinomaly: the less is more philosophy in multi-class unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20405\u201320415. (2025)","DOI":"10.1109\/CVPR52734.2025.01900"},{"key":"36_CR22","doi-asserted-by":"publisher","first-page":"7140","DOI":"10.1109\/TPAMI.2025.3570494","volume":"47","author":"H Zhang","year":"2025","unstructured":"Zhang, H., Wang, Z., Zeng, D., Wu, Z., Jiang, Y.G.: DiffusionAD: norm-guided one-step denoising diffusion for anomaly detection. IEEE Trans. Pattern Anal. Mach. Intell. 47, 7140\u20137152 (2025)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"36_CR23","doi-asserted-by":"crossref","unstructured":"Beizaee, F., Lodygensky, G.A., Desrosiers, C., Dolz, J.: Correcting deviations from normality: a reformulated diffusion model for multi-class unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19088\u201319097. (2025)","DOI":"10.1109\/CVPR52734.2025.01778"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-3403-5_36","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T20:06:53Z","timestamp":1784405213000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-3403-5_36"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,19]]},"ISBN":["9789819234028","9789819234035"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-3403-5_36","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,19]]},"assertion":[{"value":"19 July 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Toronto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 July 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2026a","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2026\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}