{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T08:19:02Z","timestamp":1772698742945,"version":"3.50.1"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032101914","type":"print"},{"value":"9783032101921","type":"electronic"}],"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"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-10192-1_12","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T00:44:03Z","timestamp":1767314643000},"page":"140-151","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ExDD: Explicit Dual Distribution Learning for\u00a0Surface Defect Detection via\u00a0Diffusion Synthesis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5095-605X","authenticated-orcid":false,"given":"Muhammad","family":"Aqeel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Federico","family":"Leonardi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0015-5534","authenticated-orcid":false,"given":"Francesco","family":"Setti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"12_CR1","doi-asserted-by":"crossref","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Asian Conference on Computer Vision (ACCV) (2019)","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"12_CR2","doi-asserted-by":"publisher","unstructured":"Aqeel, M., Sharifi, S., Cristani, M., Setti, F.: Meta learning-driven iterative refinement for robust anomaly detection in industrial inspection. In: Del Bue, A., Canton, C., Pont-Tuset, J., Tommasi, T. (eds.) ECCV 2024. LNCS, vol. 15626, pp. 445\u2013460. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-92805-5_28","DOI":"10.1007\/978-3-031-92805-5_28"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Aqeel, M., Sharifi, S., Cristani, M., Setti, F.: Self-supervised learning for robust surface defect detection. In: International Conference on Deep Learning Theory and Applications (DELTA) (2024)","DOI":"10.1007\/978-3-031-66705-3_11"},{"key":"12_CR4","doi-asserted-by":"crossref","unstructured":"Aqeel, M., Sharifi, S., Cristani, M., Setti, F.: Self-supervised iterative refinement for anomaly detection in industrial quality control. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) (2025)","DOI":"10.5220\/0013178100003912"},{"key":"12_CR5","unstructured":"Aqeel, M., Sharifi, S., Cristani, M., Setti, F.: Towards real unsupervised anomaly detection via confident meta-learning. In: IEEE\/CVF International Conference on Computer Vision (ICCV) (2025)"},{"issue":"4","key":"12_CR6","doi-asserted-by":"crossref","DOI":"10.1115\/1.4049535","volume":"21","author":"PM Bhatt","year":"2021","unstructured":"Bhatt, P.M., et al.: Image-based surface defect detection using deep learning: a review. J. Comput. Inf. Sci. Eng. 21(4), 040801 (2021)","journal-title":"J. Comput. Inf. Sci. Eng."},{"key":"12_CR7","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2021.103459","volume":"129","author":"J Bo\u017ei\u010d","year":"2021","unstructured":"Bo\u017ei\u010d, J., Tabernik, D., Sko\u010daj, D.: Mixed supervision for surface-defect detection: From weakly to fully supervised learning. Comput. Ind. 129, 103459 (2021)","journal-title":"Comput. Ind."},{"key":"12_CR8","doi-asserted-by":"crossref","unstructured":"Capogrosso, L., et\u00a0al.: Diffusion-based image generation for in-distribution data augmentation in surface defect detection. In: International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP) (2024)","DOI":"10.5220\/0012350400003660"},{"issue":"16","key":"12_CR9","doi-asserted-by":"crossref","first-page":"7657","DOI":"10.3390\/app11167657","volume":"11","author":"Y Chen","year":"2021","unstructured":"Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., Shao, L.: Surface defect detection methods for industrial products: a review. Appl. Sci. 11(16), 7657 (2021)","journal-title":"Appl. Sci."},{"key":"12_CR10","doi-asserted-by":"crossref","unstructured":"Defard, T., Setkov, A., Loesch, A., Audigier, R.: PADIM: a patch distribution modeling framework for anomaly detection and localization. In: International Conference on Pattern Recognition (ICPR) (2021)","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"12_CR11","doi-asserted-by":"crossref","unstructured":"Girella, F., Liu, Z., Fummi, F., Setti, F., Cristani, M., Capogrosso, L.: Leveraging latent diffusion models for training-free in-distribution data augmentation for surface defect detection. In: International Conference on Content-based Multimedia Indexing (CBMI) (2024)","DOI":"10.1109\/CBMI62980.2024.10858875"},{"key":"12_CR12","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1007\/s10845-020-01710-x","volume":"33","author":"S Jain","year":"2022","unstructured":"Jain, S., Seth, G., Paruthi, A., Soni, U., Kumar, G.: Synthetic data augmentation for surface defect detection and classification using deep learning. J. Intell. Manuf. 33, 1007\u20131020 (2022)","journal-title":"J. Intell. Manuf."},{"issue":"1","key":"12_CR13","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1007\/s11042-022-13308-x","volume":"82","author":"M Jawahar","year":"2023","unstructured":"Jawahar, M., Anbarasi, L.J., Geetha, S.: Vision based leather defect detection: a survey. Multimed. Tools Appl. 82(1), 989\u20131015 (2023)","journal-title":"Multimed. Tools Appl."},{"key":"12_CR14","unstructured":"Podell, D., et al.: SDXL: improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952 (2023)"},{"key":"12_CR15","doi-asserted-by":"crossref","unstructured":"Roth, K., Pemula, L., Zepeda, J., Sch\u00f6lkopf, B., Brox, T., Gehler, P.: Towards total recall in industrial anomaly detection. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"12_CR16","unstructured":"Von\u00a0Platen, P., et al.: Diffusers: state-of-the-art diffusion models (2022)"},{"key":"12_CR17","doi-asserted-by":"crossref","unstructured":"Vrochidou, E., et al.: Towards robotic marble resin application: crack detection on marble using deep learning. Electronics 11(20) (2022)","DOI":"10.3390\/electronics11203289"},{"key":"12_CR18","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In: IEEE\/CVF International Conference on Computer Vision (ICCV) (2021)","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"12_CR19","doi-asserted-by":"publisher","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: DSR\u2013a dual subspace re-projection network for surface anomaly detection. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13691. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19821-2_31","DOI":"10.1007\/978-3-031-19821-2_31"},{"key":"12_CR20","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2020.107541","volume":"153","author":"S Zhang","year":"2021","unstructured":"Zhang, S., Zhang, Q., Gu, J., Su, L., Li, K., Pecht, M.: Visual inspection of steel surface defects based on domain adaptation and adaptive convolutional neural network. Mech. Syst. Signal Process. 153, 107541 (2021)","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Lecture Notes in Computer Science","Image Analysis and Processing \u2013 ICIAP 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-10192-1_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T07:37:49Z","timestamp":1772696269000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-10192-1_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032101914","9783032101921"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-10192-1_12","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image Analysis and Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rome","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iciap2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iciap.org\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}