{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T03:17:27Z","timestamp":1740107847445,"version":"3.37.3"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T00:00:00Z","timestamp":1651017600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62072238"],"award-info":[{"award-number":["No. 62072238"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimedia Systems"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s00530-022-00932-8","type":"journal-article","created":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T15:07:18Z","timestamp":1651072038000},"page":"1667-1677","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RMVAE: one-class classification via divergence regularization and maximization mutual information"],"prefix":"10.1007","volume":"28","author":[{"given":"Chen","family":"Hong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"LongQuan","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,27]]},"reference":[{"key":"932_CR1","unstructured":"Ruff, L., Vandermeulen, R., Goernitz N., et al.: Deep one-class classification. In: Proceedings of the International Conference on Machine Learning, pp. 4393\u20134402 (2018)"},{"issue":"2","key":"932_CR2","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1109\/TCSS.2020.2970805","volume":"7","author":"Z Li","year":"2020","unstructured":"Li, Z., Liu, G., Jiang, C.: Deep representation learning with full center loss for credit card fraud detection. IEEE Trans. Comput. Soc. Syst. 7(2), 569\u2013579 (2020)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"932_CR3","doi-asserted-by":"crossref","unstructured":"Markovitz, A., Sharir, G., Friedman, I., et al.: Graph embedded pose clustering for anomaly detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10539\u201310547 (2020)","DOI":"10.1109\/CVPR42600.2020.01055"},{"key":"932_CR4","unstructured":"Zong, B., Song, Q., Min, M.R., et al.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: Proceedings of the International Conference on Learning Representations (2018)"},{"key":"932_CR5","unstructured":"Golan, I., El-Yaniv, R.: Deep anomaly detection using geometric transformations (2018). arXiv:1805.10917"},{"key":"932_CR6","doi-asserted-by":"crossref","unstructured":"Abati, D., Porrello, A., Calderara, S., et al.: Latent space autoregression for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 481\u2013490 (2019)","DOI":"10.1109\/CVPR.2019.00057"},{"key":"932_CR7","doi-asserted-by":"crossref","unstructured":"Gong, D., Liu, L., Le, V., et al.: Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1705\u20131714 (2019)","DOI":"10.1109\/ICCV.2019.00179"},{"key":"932_CR8","doi-asserted-by":"crossref","unstructured":"Perera, P., Nallapati ,R., Xiang, B.: Ocgan: One-class novelty detection using gans with constrained latent representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2898\u20132906 (2019)","DOI":"10.1109\/CVPR.2019.00301"},{"key":"932_CR9","doi-asserted-by":"crossref","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: Ganomaly: semi-supervised anomaly detection via adversarial training. In: Proceedings of the Asian Conference on Computer Vision, pp. 622\u2013637 (2018)","DOI":"10.1007\/978-3-030-20893-6_39"},{"issue":"7","key":"932_CR10","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., et al.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443\u20131471 (2001)","journal-title":"Neural Comput."},{"issue":"1","key":"932_CR11","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1023\/B:MACH.0000008084.60811.49","volume":"54","author":"DMJ Tax","year":"2004","unstructured":"Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45\u201366 (2004)","journal-title":"Mach. Learn."},{"key":"932_CR12","unstructured":"Pidhorskyi, S., Almohsen, R., Adjeroh, D.A., et al.: Generative probabilistic novelty detection with adversarial autoencoders (2018). arXiv:1807.02588"},{"key":"932_CR13","unstructured":"Nguyen, D.T., Lou, Z., Klar, M., et al.: Anomaly detection with multiple-hypotheses predictions. In: Proceedings of the International Conference on Machine Learning, pp. 4800\u20134809 (2019)"},{"key":"932_CR14","doi-asserted-by":"crossref","unstructured":"Sakurada, M., Yairi, T.: Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Proceedings of the MLSDA workshop on machine learning for sensory data analysis, pp. 4\u201311 (2014)","DOI":"10.1145\/2689746.2689747"},{"key":"932_CR15","doi-asserted-by":"crossref","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., et al.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Proceedings of the International Conference on Information Processing in Medical imaging, pp. 146\u2013157 (2017)","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"932_CR16","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Khalooei, M., Fathy, M., et al.: Adversarially learned one-class classifier for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3379\u20133388 (2018)","DOI":"10.1109\/CVPR.2018.00356"},{"key":"932_CR17","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., et al.: Eca-net: efficient channel attention for deep convolutional neural networks (2019). arXiv:1910.03151 [CoRR]","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"932_CR18","doi-asserted-by":"crossref","unstructured":"Kwon, G., Prabhushankar, M., Temel, D., et al.: Backpropagated gradient representations for anomaly detection. In: Proceedings of the European Conference on Computer Vision, pp. 206\u2013226 (2020)","DOI":"10.1007\/978-3-030-58589-1_13"},{"key":"932_CR19","doi-asserted-by":"crossref","unstructured":"Xia, Y., Cao, X., Wen, F., et al.: Learning discriminative reconstructions for unsupervised outlier removal. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1511\u20131519 (2015)","DOI":"10.1109\/ICCV.2015.177"},{"key":"932_CR20","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., et al.: LOF: identifying density-based local outliers. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 93\u2013104 (2000)","DOI":"10.1145\/335191.335388"},{"key":"932_CR21","unstructured":"Oord, A., Kalchbrenner, N., Vinyals, O., et al.: Conditional image generation with pixelcnn decoders (2016). arXiv:1606.05328"},{"key":"932_CR22","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes (2013). arXiv:1312.6114"},{"key":"932_CR23","doi-asserted-by":"crossref","unstructured":"Abati, D., Porrello, A., Calderara, S., et al.: Latent space autoregression for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 481\u2013490 (2019)","DOI":"10.1109\/CVPR.2019.00057"},{"key":"932_CR24","doi-asserted-by":"crossref","unstructured":"Yan X., Zhang H., Xu X., et al.: Learning semantic context from normal samples for unsupervised anomaly detection. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3110\u20133118 (2021)","DOI":"10.1609\/aaai.v35i4.16420"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-022-00932-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-022-00932-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-022-00932-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,25]],"date-time":"2022-09-25T12:25:22Z","timestamp":1664108722000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-022-00932-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,27]]},"references-count":24,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["932"],"URL":"https:\/\/doi.org\/10.1007\/s00530-022-00932-8","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"type":"print","value":"0942-4962"},{"type":"electronic","value":"1432-1882"}],"subject":[],"published":{"date-parts":[[2022,4,27]]},"assertion":[{"value":"30 October 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}