{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T14:55:16Z","timestamp":1785336916599,"version":"3.55.0"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030585730","type":"print"},{"value":"9783030585747","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58574-7_45","type":"book-chapter","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T16:19:07Z","timestamp":1605197947000},"page":"751-766","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Neural Batch Sampling with Reinforcement Learning for Semi-supervised Anomaly Detection"],"prefix":"10.1007","author":[{"given":"Wen-Hsuan","family":"Chu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kris M.","family":"Kitani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,13]]},"reference":[{"key":"45_CR1","doi-asserted-by":"crossref","unstructured":"Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Deep autoencoding models for unsupervised anomaly segmentation in brain MR images. In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries-4th International Workshop (2018)","DOI":"10.1007\/978-3-030-11723-8_16"},{"key":"45_CR2","unstructured":"Bengio, S., Vinyals, O., Jaitly, N., Shazeer, N.: Scheduled sampling for sequence prediction with recurrent neural networks. In: Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems (NIPS) (2015)"},{"key":"45_CR3","doi-asserted-by":"crossref","unstructured":"Bergmann, P., Fauser, M., Sattlegger, D., Steger, C.: Mvtec AD-A comprehensive real-world dataset for unsupervised anomaly detection. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2019)","DOI":"10.1109\/CVPR.2019.00982"},{"key":"45_CR4","doi-asserted-by":"crossref","unstructured":"Bergmann, P., L\u00f6we, S., Fauser, M., Sattlegger, D., Steger, C.: 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, vol. 5: VISAPP (2019)","DOI":"10.5220\/0007364500002108"},{"issue":"1","key":"45_CR5","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1134\/S1054661816010053","volume":"26","author":"T B\u00f6ttger","year":"2016","unstructured":"B\u00f6ttger, T., Ulrich, M.: Real-time texture error detection on textured surfaces with compressed sensing. Pattern Recogn. Image Anal. 26(1), 88\u201394 (2016). https:\/\/doi.org\/10.1134\/S1054661816010053","journal-title":"Pattern Recogn. Image Anal."},{"key":"45_CR6","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1109\/TII.2016.2641472","volume":"13","author":"D Carrera","year":"2017","unstructured":"Carrera, D., Manganini, F., Boracchi, G., Lanzarone, E.: Defect detection in SEM images of nanofibrous materials. IEEE Trans. Ind. Inf. 13, 551 (2017)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"4","key":"45_CR7","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L Chen","year":"2018","unstructured":"Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834\u2013848 (2018)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"45_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-3-319-24474-7_14","volume-title":"Data Science","author":"L Cui","year":"2015","unstructured":"Cui, L., Qi, Z., Chen, Z., Meng, F., Shi, Y.: Pavement distress detection using random decision forests. In: Zhang, C., et al. (eds.) ICDS 2015. LNCS, vol. 9208, pp. 95\u2013102. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24474-7_14"},{"key":"45_CR9","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"45_CR10","unstructured":"Eskin, E.: Anomaly detection over noisy data using learned probability distributions. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML) (2000)"},{"key":"45_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"45_CR12","unstructured":"Kr\u00e4henb\u00fchl, P., Koltun, V.: Efficient inference in fully connected CRFS with gaussian edge potentials. In: Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems (NIPS) (2011)"},{"issue":"12","key":"45_CR13","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1016\/j.sigpro.2003.07.018","volume":"83","author":"M Markou","year":"2003","unstructured":"Markou, M., Singh, S.: Novelty detection: a review\u2014part 1: statistical approaches. Signal Process. 83(12), 2481\u20132497 (2003)","journal-title":"Signal Process."},{"key":"45_CR14","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.: Anomaly detection in nanofibrous materials by CNN-based self-similarity. Sensors 18, 209 (2018)","journal-title":"Sensors"},{"issue":"23","key":"45_CR15","doi-asserted-by":"publisher","first-page":"6260","DOI":"10.1109\/TSP.2017.2749215","volume":"65","author":"M Rahmani","year":"2017","unstructured":"Rahmani, M., Atia, G.K.: Coherence pursuit: fast, simple, and robust principal component analysis. IEEE Trans. Signal Process. 65(23), 6260\u20136275 (2017)","journal-title":"IEEE Trans. Signal Process."},{"key":"45_CR16","doi-asserted-by":"crossref","unstructured":"Ravanbakhsh, M., Sangineto, E., Nabi, M., Sebe, N.: Training adversarial discriminators for cross-channel abnormal event detection in crowds. In: IEEE Winter Conference on Applications of Computer Vision, WACV (2019)","DOI":"10.1109\/WACV.2019.00206"},{"key":"45_CR17","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2014 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"45_CR18","unstructured":"Ross, S., Gordon, G., Bagnell, D.: A reduction of imitation learning and structured prediction to no-regret online learning. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 627\u2013635 (2011)"},{"key":"45_CR19","unstructured":"Ruff, L., et al.: Deep semi-supervised anomaly detection (2020)"},{"key":"45_CR20","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection. In: 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2018)","DOI":"10.1109\/CVPR.2018.00356"},{"key":"45_CR21","doi-asserted-by":"crossref","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Information Processing in Medical Imaging-25th International Conference, IPMI (2017)","DOI":"10.1007\/978-3-319-59050-9_12"},{"issue":"4","key":"45_CR22","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","volume":"39","author":"E Shelhamer","year":"2017","unstructured":"Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 640\u2013651 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"12","key":"45_CR23","doi-asserted-by":"publisher","first-page":"3434","DOI":"10.1109\/TITS.2016.2552248","volume":"17","author":"Y Shi","year":"2016","unstructured":"Shi, Y., Cui, L., Qi, Z., Meng, F., Chen, Z.: Automatic road crack detection using random structured forests. IEEE Trans. Intell. Transp. Syst. 17(12), 3434\u20133445 (2016)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"45_CR24","volume-title":"Machine Vision Algorithms and Applications","author":"C Steger","year":"2018","unstructured":"Steger, C., Ulrich, M., Wiedemann, C.: Machine Vision Algorithms and Applications. John Wiley & Sons, Hoboken (2018)"},{"key":"45_CR25","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement learning-an introduction. In: Adaptive Computation and Machine Learning MIT Press, New York (1998)"},{"key":"45_CR26","first-page":"229","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8, 229\u2013256 (1992)","journal-title":"Mach. Learn."},{"key":"45_CR27","unstructured":"Xu, H., Caramanis, C., Sanghavi, S.: Robust PCA via outlier pursuit. In: Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems (NIPS) (2010)"},{"issue":"3","key":"45_CR28","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1023\/B:DAMI.0000023676.72185.7c","volume":"8","author":"K Yamanishi","year":"2004","unstructured":"Yamanishi, K., Takeuchi, J.I., Williams, G., Milne, P.: On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Min. Knowl. Disc. 8(3), 275\u2013300 (2004)","journal-title":"Data Min. Knowl. Disc."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58574-7_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,12]],"date-time":"2024-11-12T00:14:12Z","timestamp":1731370452000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58574-7_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030585730","9783030585747"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58574-7_45","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"13 November 2020","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":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}