{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T20:02:32Z","timestamp":1742932952906,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031263866"},{"type":"electronic","value":"9783031263873"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-26387-3_17","type":"book-chapter","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T15:03:10Z","timestamp":1678978990000},"page":"275-290","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Anomaly Detection via\u00a0Few-Shot Learning on\u00a0Normality"],"prefix":"10.1007","author":[{"given":"Shin","family":"Ando","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayaka","family":"Yamamoto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"key":"17_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":"17_CR2","unstructured":"Alemi, A.A., Fischer, I., Dillon, J.V., Murphy, K.: Deep variational information bottleneck. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017)"},{"key":"17_CR3","unstructured":"Ando, S.: Deep representation learning with an information-theoretic loss. CoRR abs\/2111.12950 (2021)"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Ding, R., Guo, G., Yang, X., Chen, B., Liu, Z., He, X.: BiGAN: collaborative filtering with bidirectional generative adversarial networks. In: Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020, pp. 82\u201390. SIAM (2020)","DOI":"10.1137\/1.9781611976236.10"},{"key":"17_CR5","doi-asserted-by":"crossref","unstructured":"Ghafoori, Z., Leckie, C.: Deep multi-sphere support vector data description. In: Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020, pp. 109\u2013117. SIAM (2020)","DOI":"10.1137\/1.9781611976236.13"},{"key":"17_CR6","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. NIPS\u201914, vol. 2, pp. 2672\u20132680. MIT Press, Cambridge (2014)"},{"key":"17_CR7","unstructured":"Jeong, T., Kim, H.: OOD-MAML: meta-learning for few-shot out-of-distribution detection and classification. In: Advances in Neural Information Processing Systems, vol. 33, pp. 3907\u20133916. Curran Associates, Inc. (2020)"},{"key":"17_CR8","doi-asserted-by":"crossref","unstructured":"Kwon, D., Kim, H., Kim, J., Suh, S.C., Kim, I., Kim, K.J.: A survey of deep learning-based network anomaly detection. Clust. Comput. (2017)","DOI":"10.1007\/s10586-017-1117-8"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Lee, D., Yu, S., Yu, H.: Multi-class data description for out-of-distribution detection. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201920, pp. 1362\u20131370. Association for Computing Machinery, New York (2020)","DOI":"10.1145\/3394486.3403189"},{"key":"17_CR10","doi-asserted-by":"crossref","unstructured":"McInnes, L., Healy, J., Saul, N., Gro\u00dfberger, L.: UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3(29), 861 (2018)","DOI":"10.21105\/joss.00861"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(2) (Mar 2021)","DOI":"10.1145\/3439950"},{"key":"17_CR12","unstructured":"Ruff, L., et al.: Deep one-class classification. In: Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 4393\u20134402. PMLR (2018)"},{"key":"17_CR13","unstructured":"Ruff, L., et al.: Deep semi-supervised anomaly detection. In: 8th International Conference on Learning Representations, ICLR 2020. OpenReview.net (2020)"},{"key":"17_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-319-59050-9_12","volume-title":"Information Processing in Medical Imaging","author":"T Schlegl","year":"2017","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: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146\u2013157. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54, 45\u201366 (2004)","DOI":"10.1023\/B:MACH.0000008084.60811.49"},{"key":"17_CR16","doi-asserted-by":"crossref","unstructured":"Tishby, N., Zaslavsky, N.: Deep learning and the information bottleneck principle. In: 2015 IEEE Information Theory Workshop (ITW), pp. 1\u20135 (2015)","DOI":"10.1109\/ITW.2015.7133169"},{"key":"17_CR17","unstructured":"Tishby, N., Pereira, F.C., Bialek, W.: The information bottleneck method. Comput. Res. Repos. (CoRR) physics\/0004057 (2000)"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Zenati, H., Romain, M., Foo, C., Lecouat, B., Chandrasekhar, V.: Adversarially learned anomaly detection. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 727\u2013736 (2018)","DOI":"10.1109\/ICDM.2018.00088"},{"key":"17_CR19","unstructured":"Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection. CoRR abs\/1802.06222 (2018),"},{"key":"17_CR20","unstructured":"Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection (2019)"},{"issue":"11","key":"17_CR21","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"17_CR22","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms, August 2017"},{"key":"17_CR23","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. Master\u2019s thesis (2009)"},{"key":"17_CR24","doi-asserted-by":"crossref","unstructured":"Liu, B., Kang, H., Li, H., Hua, G., Vasconcelos, N.: Few-shot open-set recognition using meta-learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020","DOI":"10.1109\/CVPR42600.2020.00882"},{"key":"17_CR25","doi-asserted-by":"crossref","unstructured":"Jeong, M., Choi, S., Kim, C.: Few-shot open-set recognition by transformation consistency. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 12566\u201312575, June 2021","DOI":"10.1109\/CVPR46437.2021.01238"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26387-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T15:06:48Z","timestamp":1678979208000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26387-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263866","9783031263873"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26387-3_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","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":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","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":"236","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":"22% - 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":"3-4","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":"3-4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 demo track papers have been accepted from 28 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)"}}]}}