{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T09:48:48Z","timestamp":1784454528989,"version":"3.55.0"},"publisher-location":"Cham","reference-count":41,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031728549","type":"print"},{"value":"9783031728556","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T00:00:00Z","timestamp":1731110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,9]],"date-time":"2024-11-09T00:00:00Z","timestamp":1731110400000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-72855-6_3","type":"book-chapter","created":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T18:49:29Z","timestamp":1731091769000},"page":"37-54","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["A Unified Anomaly Synthesis Strategy with\u00a0Gradient Ascent for\u00a0Industrial Anomaly Detection and\u00a0Localization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7910-862X","authenticated-orcid":false,"given":"Qiyu","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7855-9902","authenticated-orcid":false,"given":"Huiyuan","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5319-1363","authenticated-orcid":false,"given":"Chengkan","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1659-7879","authenticated-orcid":false,"given":"Zhengtao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,9]]},"reference":[{"key":"3_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-20893-6_39","volume-title":"Computer Vision \u2013 ACCV 2018","author":"S Akcay","year":"2019","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622\u2013637. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_39"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Bae, J., Lee, J.H., Kim, S.: PNI: industrial anomaly detection using position and neighborhood information. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6373\u20136383 (2023)","DOI":"10.1109\/ICCV51070.2023.00586"},{"key":"3_CR3","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, pp. 128\u2013138 (2024)","DOI":"10.1109\/WACV57701.2024.00020"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: MVTec AD\u2013a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9592\u20139600 (2019)","DOI":"10.1109\/CVPR.2019.00982"},{"issue":"11","key":"3_CR5","doi-asserted-by":"publisher","first-page":"10674","DOI":"10.1109\/TII.2023.3241579","volume":"19","author":"Y Cao","year":"2023","unstructured":"Cao, Y., Xu, X., Liu, Z., Shen, W.: Collaborative discrepancy optimization for reliable image anomaly localization. IEEE Trans. Industr. Inf. 19(11), 10674\u201310683 (2023)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3606\u20133613 (2014)","DOI":"10.1109\/CVPR.2014.461"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition workshops, pp. 702\u2013703 (2020)","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"3_CR8","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, pp. 9737\u20139746 (2022)","DOI":"10.1109\/CVPR52688.2022.00951"},{"key":"3_CR9","unstructured":"Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using real NVP. In: International Conference on Learning Representations (2017)"},{"key":"3_CR10","unstructured":"Goyal, S., Raghunathan, A., Jain, M., Simhadri, H.V., Jain, P.: DROCC: deep robust one-class classification. In: International Conference on Machine Learning, vol.\u00a0119, pp. 3711\u20133721. PMLR (2020)"},{"key":"3_CR11","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, pp. 98\u2013107 (2022)","DOI":"10.1109\/WACV51458.2022.00188"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Hou, J., Zhang, Y., Zhong, Q., Xie, D., Pu, S., Zhou, H.: Divide-and-assemble: learning block-wise memory for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8791\u20138800 (2021)","DOI":"10.1109\/ICCV48922.2021.00867"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Hyun, J., Kim, S., Jeon, G., Kim, S.H., Bae, K., Kang, B.J.: Reconpatch: contrastive patch representation learning for industrial anomaly detection. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2052\u20132061 (2024)","DOI":"10.1109\/WACV57701.2024.00205"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Jezek, S., Jonak, M., Burget, R., Dvorak, P., Skotak, M.: Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions. In: International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), pp. 66\u201371. IEEE (2021)","DOI":"10.1109\/ICUMT54235.2021.9631567"},{"key":"3_CR15","doi-asserted-by":"publisher","first-page":"78446","DOI":"10.1109\/ACCESS.2022.3193699","volume":"10","author":"S Lee","year":"2022","unstructured":"Lee, S., Lee, S., Song, B.C.: CFA: coupled-hypersphere-based feature adaptation for target-oriented anomaly localization. IEEE Access 10, 78446\u201378454 (2022)","journal-title":"IEEE Access"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Lei, J., Hu, X., Wang, Y., Liu, D.: Pyramidflow: high-resolution defect contrastive localization using pyramid normalizing flow. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14143\u201314152 (2023)","DOI":"10.1109\/CVPR52729.2023.01359"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Li, C.L., Sohn, K., Yoon, J., Pfister, T.: Cutpaste: Self-supervised learning for anomaly detection and localization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9664\u20139674 (2021)","DOI":"10.1109\/CVPR46437.2021.00954"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll\u00e1r, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.324"},{"issue":"1","key":"3_CR19","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/s11633-023-1459-z","volume":"21","author":"J Liu","year":"2024","unstructured":"Liu, J., et al.: Deep industrial image anomaly detection: a survey. Mach. Intell. Res. 21(1), 104\u2013135 (2024). https:\/\/doi.org\/10.1007\/s11633-023-1459-z","journal-title":"Mach. Intell. Res."},{"key":"3_CR20","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, pp. 20402\u201320411 (2023)","DOI":"10.1109\/CVPR52729.2023.01954"},{"key":"3_CR21","doi-asserted-by":"publisher","first-page":"83","DOI":"10.2197\/ipsjtcva.1.83","volume":"1","author":"R Pless","year":"2009","unstructured":"Pless, R., Souvenir, R.: A survey of manifold learning for images. IPSJ Trans. Comput. Vis. Appl. 1, 83\u201394 (2009)","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Reiss, T., Cohen, N., Bergman, L., Hoshen, Y.: Panda: adapting pretrained features for anomaly detection and segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2806\u20132814 (2021)","DOI":"10.1109\/CVPR46437.2021.00283"},{"key":"3_CR23","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: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14318\u201314328 (2022)","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"3_CR24","unstructured":"Ruff, L., et al.: Deep one-class classification. In: International Conference on Machine Learning, pp. 4393\u20134402. PMLR (2018)"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multiresolution knowledge distillation for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14902\u201314912 (2021)","DOI":"10.1109\/CVPR46437.2021.01466"},{"key":"3_CR26","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/978-3-031-19821-2_27","volume-title":"ECCV","author":"HM Schl\u00fcter","year":"2022","unstructured":"Schl\u00fcter, H.M., Tan, J., Hou, B., Kainz, B.: Natural synthetic anomalies for self-supervised anomaly detection and localization. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV. LNCS, vol. 13691, pp. 474\u2013489. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19821-2_27"},{"issue":"7","key":"3_CR27","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443\u20131471 (2001)","journal-title":"Neural Comput."},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 761\u2013769 (2016)","DOI":"10.1109\/CVPR.2016.89"},{"key":"3_CR29","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"DM Tax","year":"2004","unstructured":"Tax, D.M., Duin, R.P.: Support vector data description. Mach. Learn. 54, 45\u201366 (2004)","journal-title":"Mach. Learn."},{"key":"3_CR30","doi-asserted-by":"crossref","unstructured":"Tien, T.D., et al.: Revisiting reverse distillation for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24511\u201324520 (2023)","DOI":"10.1109\/CVPR52729.2023.02348"},{"key":"3_CR31","unstructured":"Xiao, F., Sun, R., Fan, J.: Restricted generative projection for one-class classification and anomaly detection. arXiv preprint arXiv:2307.04097 (2023)"},{"key":"3_CR32","doi-asserted-by":"publisher","first-page":"105835","DOI":"10.1016\/j.engappai.2023.105835","volume":"119","author":"M Yang","year":"2023","unstructured":"Yang, M., Wu, P., Feng, H.: MemSeg: a semi-supervised method for image surface defect detection using differences and commonalities. Eng. Appl. Artif. Intell. 119, 105835 (2023)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"3_CR33","doi-asserted-by":"crossref","unstructured":"Yao, X., Li, R., Zhang, J., Sun, J., Zhang, C.: Explicit boundary guided semi-push-pull contrastive learning for supervised anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 24490\u201324499 (2023)","DOI":"10.1109\/CVPR52729.2023.02346"},{"key":"3_CR34","unstructured":"You, Z., et al.: A unified model for multi-class anomaly detection. In: Advance in Neural Information Processing System, vol. 35, pp. 4571\u20134584 (2022)"},{"key":"3_CR35","unstructured":"Yu, J., et al.: Fastflow: unsupervised anomaly detection and localization via 2D normalizing flows. arXiv preprint arXiv:2111.07677 (2021)"},{"key":"3_CR36","doi-asserted-by":"crossref","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: DRAEM-a discriminatively trained reconstruction embedding for surface anomaly detection. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8330\u20138339 (2021)","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"3_CR37","doi-asserted-by":"publisher","first-page":"107706","DOI":"10.1016\/j.patcog.2020.107706","volume":"112","author":"V Zavrtanik","year":"2021","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: Reconstruction by inpainting for visual anomaly detection. Pattern Recogn. 112, 107706 (2021)","journal-title":"Pattern Recogn."},{"key":"3_CR38","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1007\/978-3-031-19821-2_31","volume-title":"ECCV 2022","author":"V Zavrtanik","year":"2022","unstructured":"Zavrtanik, V., Kristan, M., Sko\u010daj, D.: DSR-a 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, pp. 539\u2013554. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19821-2_31"},{"key":"3_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, X., Li, S., Li, X., Huang, P., Shan, J., Chen, T.: DeSTSeg: segmentation guided denoising student-teacher for anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3914\u20133923 (2023)","DOI":"10.1109\/CVPR52729.2023.00381"},{"key":"3_CR40","doi-asserted-by":"crossref","unstructured":"Zhou, Y.: Rethinking reconstruction autoencoder-based out-of-distribution detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7379\u20137387 (2022)","DOI":"10.1109\/CVPR52688.2022.00723"},{"key":"3_CR41","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1007\/978-3-031-20056-4_23","volume-title":"ECCV 2022","author":"Y Zou","year":"2022","unstructured":"Zou, Y., Jeong, J., Pemula, L., Zhang, D., Dabeer, O.: Spot-the-difference self-supervised pre-training for anomaly detection and segmentation. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13690, pp. 392\u2013408. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-20056-4_23"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72855-6_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T19:03:01Z","timestamp":1731092581000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72855-6_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,9]]},"ISBN":["9783031728549","9783031728556"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72855-6_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,9]]},"assertion":[{"value":"9 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}