{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T13:03:26Z","timestamp":1746709406818,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030921842"},{"type":"electronic","value":"9783030921859"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-92185-9_9","type":"book-chapter","created":{"date-parts":[[2021,12,5]],"date-time":"2021-12-05T17:02:46Z","timestamp":1638723766000},"page":"103-115","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Stack Multiple Shallow Autoencoders into\u00a0a\u00a0Strong One: A\u00a0New Reconstruction-Based Method to\u00a0Detect Anomaly"],"prefix":"10.1007","author":[{"given":"Hanqi","family":"Wang","sequence":"first","affiliation":[]},{"given":"Xing","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Liang","family":"Song","sequence":"additional","affiliation":[]},{"given":"Guanhua","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Linhua","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"issue":"1","key":"9_CR1","first-page":"1","volume":"2","author":"J An","year":"2015","unstructured":"An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Spec. Lect. IE 2(1), 1\u201318 (2015)","journal-title":"Spec. Lect. IE"},{"key":"9_CR2","first-page":"153","volume":"19","author":"Y Bengio","year":"2007","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., et al.: Greedy layer-wise training of deep networks. Adv. Neural. Inf. Process. Syst. 19, 153 (2007)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR3","unstructured":"Chalapathy, R., Menon, A.K., Chawla, S.: Anomaly detection using one-class neural networks. arXiv preprint arXiv:1802.06360 (2018)"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Gong, D., et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: ICCV, pp. 1705\u20131714 (2019)","DOI":"10.1109\/ICCV.2019.00179"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Hasan, M., Choi, J., Neumann, J., Roy-Chowdhury, A.K., Davis, L.S.: Learning temporal regularity in video sequences. In: CVPR, pp. 733\u2013742 (2016)","DOI":"10.1109\/CVPR.2016.86"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Hinami, R., Mei, T., Satoh, S.: Joint detection and recounting of abnormal events by learning deep generic knowledge. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.391"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Kim, J., Grauman, K.: Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: CVPR, pp. 2921\u20132928. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206569"},{"key":"9_CR8","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"9_CR9","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/978-3-030-36802-9_30","volume-title":"Neural Information Processing","author":"C Li","year":"2019","unstructured":"Li, C., Xu, Q., Peng, C., Guo, Y.: Anomaly detection based on the global-local anomaly score for trajectory data. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. CCIS, vol. 1143, pp. 275\u2013285. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-36802-9_30"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Liu, W., Luo, W., Lian, D., Gao, S.: Future frame prediction for anomaly detection-a new baseline. In: CVPR, pp. 6536\u20136545 (2018)","DOI":"10.1109\/CVPR.2018.00684"},{"key":"9_CR11","doi-asserted-by":"crossref","unstructured":"Luo, W., Liu, W., Gao, S.: A revisit of sparse coding based anomaly detection in stacked RNN framework. In: ICCV, pp. 341\u2013349 (2017)","DOI":"10.1109\/ICCV.2017.45"},{"key":"9_CR12","doi-asserted-by":"crossref","unstructured":"Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1975\u20131981. IEEE (2010)","DOI":"10.1109\/CVPR.2010.5539872"},{"key":"9_CR13","unstructured":"van den Oord, A., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., Kavukcuoglu, K.: Conditional image generation with pixelcnn decoders. In: NeurIPS, pp. 4797\u20134805 (2016)"},{"issue":"2","key":"9_CR14","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":"9_CR15","doi-asserted-by":"crossref","unstructured":"Park, H., Noh, J., Ham, B.: Learning memory-guided normality for anomaly detection. In: ICCV, pp. 14372\u201314381 (2020)","DOI":"10.1109\/CVPR42600.2020.01438"},{"issue":"3","key":"9_CR16","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1214\/aoms\/1177704472","volume":"33","author":"E Parzen","year":"1962","unstructured":"Parzen, E.: On estimation of a probability density function and mode. Ann. Math. Stat. 33(3), 1065\u20131076 (1962)","journal-title":"Ann. Math. Stat."},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Ravanbakhsh, M., Nabi, M., Sangineto, E., Marcenaro, L., Regazzoni, C., Sebe, N.: Abnormal event detection in videos using generative adversarial nets. In: ICIP, pp. 1577\u20131581. IEEE (2017)","DOI":"10.1109\/ICIP.2017.8296547"},{"key":"9_CR18","unstructured":"Ruff, L., et al.: Deep one-class classification. In: ICML, pp. 4393\u20134402. PMLR (2018)"},{"key":"9_CR19","unstructured":"Sch\u00f6lkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C., et al.: Support vector method for novelty detection. In: NeurIPS (1999)"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Tudor Ionescu, R., Smeureanu, S., Alexe, B., Popescu, M.: Unmasking the abnormal events in video. In: ICCV, pp. 2895\u20132903 (2017)","DOI":"10.1109\/ICCV.2017.315"},{"key":"9_CR21","doi-asserted-by":"crossref","unstructured":"Xu, D., Ricci, E., Yan, Y., Song, J., Sebe, N.: Learning deep representations of appearance and motion for anomalous event detection. arXiv preprint arXiv:1510.01553 (2015)","DOI":"10.5244\/C.29.8"},{"key":"9_CR22","doi-asserted-by":"publisher","first-page":"41238","DOI":"10.1109\/ACCESS.2018.2858277","volume":"6","author":"B Yan","year":"2018","unstructured":"Yan, B., Han, G.: Effective feature extraction via stacked sparse autoencoder to improve intrusion detection system. IEEE Access 6, 41238\u201341248 (2018)","journal-title":"IEEE Access"},{"key":"9_CR23","unstructured":"Zhai, S., Cheng, Y., Lu, W., Zhang, Z.: Deep structured energy based models for anomaly detection. In: ICML, pp. 1100\u20131109. PMLR (2016)"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Deng, B., Shen, C., Liu, Y., Lu, H., Hua, X.S.: Spatio-temporal autoencoder for video anomaly detection. In: ACM MM, pp. 1933\u20131941 (2017)","DOI":"10.1145\/3123266.3123451"},{"issue":"5","key":"9_CR25","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1002\/sam.11161","volume":"5","author":"A Zimek","year":"2012","unstructured":"Zimek, A., Schubert, E., Kriegel, H.P.: A survey on unsupervised outlier detection in high-dimensional numerical data. Stat. Anal. Data Min. ASA Data Sci. J. 5(5), 363\u2013387 (2012)","journal-title":"Stat. Anal. Data Min. ASA Data Sci. J."},{"key":"9_CR26","unstructured":"Zong, B., et al.: Deep autoencoding gaussian mixture model for 3 unsupervised anomaly detection. In: ICLR (2018)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-92185-9_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T18:41:29Z","timestamp":1710355289000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-92185-9_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030921842","9783030921859"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-92185-9_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"6 December 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sanur, Bali","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2021.apnns.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1093","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":"226","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":"177","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":"21% - 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":"2.57","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":"6","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":"Due to the COVID-19 pandemic the conference was held online.","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)"}}]}}