{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T20:18:10Z","timestamp":1768508290203,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819556786","type":"print"},{"value":"9789819556793","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\/978-981-95-5679-3_18","type":"book-chapter","created":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T18:36:39Z","timestamp":1768329399000},"page":"255-269","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Revisiting Symmetric Teacher-Student Network Distillation for\u00a0Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Qunyi","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Jiaqi","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Guoyang","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Liewen","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Yongming","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xiaoning","family":"Lei","sequence":"additional","affiliation":[]},{"given":"Annan","family":"Shu","sequence":"additional","affiliation":[]},{"given":"Guannan","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Songan","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"18_CR1","unstructured":"Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M.H., Rabiee, H.R.: Multiresolution knowledge distillation for anomaly detection (2020). https:\/\/arxiv.org\/abs\/2011.11108"},{"key":"18_CR2","doi-asserted-by":"crossref","unstructured":"Deng, H., Li, X.: Anomaly detection via reverse distillation from one-class embedding (2022). https:\/\/arxiv.org\/abs\/2201.10703","DOI":"10.1109\/CVPR52688.2022.00951"},{"key":"18_CR3","unstructured":"Rudolph, M., Wehrbein, T., Rosenhahn, B., Wandt, B.: Asymmetric student-teacher networks for industrial anomaly detection (2022). https:\/\/arxiv.org\/abs\/2210.07829"},{"key":"18_CR4","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Uninformed students: student-teacher anomaly detection with discriminative latent embeddings. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.00424"},{"key":"18_CR5","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale (2021). https:\/\/arxiv.org\/abs\/2010.11929"},{"key":"18_CR6","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec ad \u2013 a comprehensive real-world dataset for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00982"},{"key":"18_CR7","doi-asserted-by":"crossref","unstructured":"Zou, Y., Jeong, J., Pemula, L., Zhang, D.,\u00a0Dabeer, O.: Spot-the-difference self-supervised pre-training for anomaly detection and segmentation (2022). https:\/\/arxiv.org\/abs\/2207.14315","DOI":"10.1007\/978-3-031-20056-4_23"},{"key":"18_CR8","doi-asserted-by":"crossref","unstructured":"Mishra, P., Verk, R., Fornasier, D., Piciarelli, C., Foresti, G.L.: VT-ADL: a vision transformer network for image anomaly detection and localization. In: 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), pp. 01\u201306. IEEE (2021). http:\/\/dx.doi.org\/10.1109\/ISIE45552.2021.9576231","DOI":"10.1109\/ISIE45552.2021.9576231"},{"issue":"2","key":"18_CR9","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. (CSUR) 54(2), 1\u201338 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"18_CR10","doi-asserted-by":"crossref","unstructured":"Wang, C., et al.: Real-IAD: a real-world multi-view dataset for benchmarking versatile industrial anomaly detection (2024). https:\/\/arxiv.org\/abs\/2403.12580","DOI":"10.1109\/CVPR52733.2024.02159"},{"issue":"1","key":"18_CR11","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)","journal-title":"Mach. Intell. Res."},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Li, C.-L., Sohn, K.,\u00a0Yoon, J.,\u00a0Pfister, 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":"18_CR13","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhou, Y., Xu, Y., Wang, Z.: Simplenet: a simple network for image anomaly detection and localization. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20:402\u201320:411 (2023). https:\/\/api.semanticscholar.org\/CorpusID:257766673","DOI":"10.1109\/CVPR52729.2023.01954"},{"key":"18_CR14","doi-asserted-by":"crossref","unstructured":"Lei, J., Hu, X., Wang, Y., Liu, D.: Pyramidflow: high-resolution defect contrastive localization using pyramid normalizing flow. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14:143\u201314:152 (2023)","DOI":"10.1109\/CVPR52729.2023.01359"},{"key":"18_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 (2022). https:\/\/arxiv.org\/abs\/2106.08265","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"18_CR16","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1007\/978-981-97-8493-6_21","volume-title":"PRCV 2024","author":"K Jiao","year":"2025","unstructured":"Jiao, K., Yao, X., Wang, L., Zhang, B., Liu, Z., Zhang, C.: Enhanced anomaly detection using spatial-alignment and multi-scale fusion. In: Lin, Z., et al. (eds.) PRCV 2024. LNCS, vol. 15043, pp. 294\u2013308. Springer, Singapore (2025). https:\/\/doi.org\/10.1007\/978-981-97-8493-6_21"},{"key":"18_CR17","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":"18_CR18","doi-asserted-by":"crossref","unstructured":"Schl\u00fcter, H.M., Tan, J., Hou, B., Kainz, B.: Natural synthetic anomalies for self-supervised anomaly detection and localization (2021)","DOI":"10.1007\/978-3-031-19821-2_27"},{"key":"18_CR19","doi-asserted-by":"crossref","unstructured":"Duan, Y., Hong, Y., Niu, L., Zhang, L.: Few-shot defect image generation via defect-aware feature manipulation (2023). https:\/\/arxiv.org\/abs\/2303.02389","DOI":"10.1609\/aaai.v37i1.25132"},{"key":"18_CR20","doi-asserted-by":"crossref","unstructured":"Lee, Y., Kang, P.: Anovit: unsupervised anomaly detection and localization with vision transformer-based encoder-decoder (2022). https:\/\/arxiv.org\/abs\/2203.10808","DOI":"10.1109\/ACCESS.2022.3171559"},{"key":"18_CR21","unstructured":"Zhang, H., Wang, Z., Wu, Z., Jiang, Y.-G.: Diffusionad: norm-guided one-step denoising diffusion for anomaly detection (2023). https:\/\/arxiv.org\/abs\/2303.08730"},{"key":"18_CR22","doi-asserted-by":"crossref","unstructured":"Wang, G., Han, S., Ding, E., Huang, D.: Student-teacher feature pyramid matching for anomaly detection (2021). https:\/\/arxiv.org\/abs\/2103.04257","DOI":"10.5244\/C.35.349"},{"key":"18_CR23","doi-asserted-by":"crossref","unstructured":"Cao, Y., Wan, Q., Shen, W., Gao, L.: Informative knowledge distillation for image anomaly segmentation. Knowl.-Based Syst. 248, 108846 (2022). https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0950705122004038","DOI":"10.1016\/j.knosys.2022.108846"},{"key":"18_CR24","doi-asserted-by":"crossref","unstructured":"Sun, Z., Li, X., Li, Y., Ma, Y.: Memoryless multimodal anomaly detection via student-teacher network and signed distance learning (2024). https:\/\/arxiv.org\/abs\/2409.05378","DOI":"10.1007\/978-981-97-8858-3_31"},{"key":"18_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1007\/978-981-97-8493-6_19","volume-title":"Pattern Recognition and Computer Vision - PRCV 2024","author":"W Li","year":"2025","unstructured":"Li, W., Huang, R., Wang, Z.: Dual-teacher network with SSIM based reverse distillation for anomaly detection. In: Lin, Z., et al. (eds.) PRCV 2024. LNCS, vol. 15043, pp. 266\u2013279. Springer, Singapore (2025). https:\/\/doi.org\/10.1007\/978-981-97-8493-6_19"},{"key":"18_CR26","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners (2021). https:\/\/arxiv.org\/abs\/2111.06377"},{"key":"18_CR27","unstructured":"Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., Vedaldi, A.: Describing textures in the wild (2013). https:\/\/arxiv.org\/abs\/1311.3618"},{"key":"18_CR28","doi-asserted-by":"crossref","unstructured":"Lee, S., Lee, S., Song, B.C.: CFA: coupled-hypersphere-based feature adaptation for target-oriented anomaly localization (2022). https:\/\/arxiv.org\/abs\/2206.04325","DOI":"10.1109\/ACCESS.2022.3193699"},{"key":"18_CR29","doi-asserted-by":"crossref","unstructured":"Rudolph, M., Wehrbein, T., Rosenhahn, B., Wandt, B.: Fully convolutional cross-scale-flows for image-based defect detection (2021). https:\/\/arxiv.org\/abs\/2110.02855","DOI":"10.1109\/WACV51458.2022.00189"},{"key":"18_CR30","unstructured":"Yu, J., et al.: Fastflow: unsupervised anomaly detection and localization via 2D normalizing flows (2021). https:\/\/arxiv.org\/abs\/2111.07677"},{"key":"18_CR31","unstructured":"Dehaene, D., Eline, P.: Anomaly localization by modeling perceptual features (2020). https:\/\/arxiv.org\/abs\/2008.05369"},{"key":"18_CR32","doi-asserted-by":"crossref","unstructured":"Defard, T., Setkov, A., Loesch, A., Audigier, R.: Padim: a patch distribution modeling framework for anomaly detection and localization (2020). https:\/\/arxiv.org\/abs\/2011.08785","DOI":"10.1007\/978-3-030-68799-1_35"},{"issue":"3","key":"18_CR33","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1109\/TII.2022.3182385","volume":"19","author":"Q Wan","year":"2023","unstructured":"Wan, Q., Gao, L., Li, X., Wen, L.: Unsupervised image anomaly detection and segmentation based on pretrained feature mapping. IEEE Trans. Industr. Inf. 19(3), 2330\u20132339 (2023)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"18_CR34","unstructured":"Cohen, N., Hoshen, Y.: Sub-image anomaly detection with deep pyramid correspondences (2021). https:\/\/arxiv.org\/abs\/2005.02357"},{"key":"18_CR35","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Unsupervised continual anomaly detection with contrastively-learned prompt. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 4, pp. 3639\u20133647 (2024)","DOI":"10.1609\/aaai.v38i4.28153"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-5679-3_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T18:36:43Z","timestamp":1768329403000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-5679-3_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819556786","9789819556793"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-5679-3_18","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":"14 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2025.prcv.cn\/index.asp","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}