{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T15:00:27Z","timestamp":1786978827543,"version":"build-2736575974"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031189067","type":"print"},{"value":"9783031189074","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-18907-4_16","type":"book-chapter","created":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T19:03:53Z","timestamp":1666811033000},"page":"205-219","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Adversarial VAE with\u00a0Normalizing Flows for\u00a0Multi-Dimensional Classification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1626-7351","authenticated-orcid":false,"given":"Wenbo","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1352-794X","authenticated-orcid":false,"given":"Yunhao","family":"Gou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1444-7183","authenticated-orcid":false,"given":"Yuepeng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1100-4835","authenticated-orcid":false,"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,27]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Bai, J., Kong, S., Gomes, C.: Disentangled variational autoencoder based multi-label classification with covariance-aware multivariate probit model. arXiv preprint arXiv:2007.06126 (2020)","DOI":"10.24963\/ijcai.2020\/595"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Chen, C., Wang, H., Liu, W., Zhao, X., Hu, T., Chen, G.: Two-stage label embedding via neural factorization machine for multi-label classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3304\u20133311 (2019)","DOI":"10.1609\/aaai.v33i01.33013304"},{"key":"16_CR3","unstructured":"Chen, R.T.Q., Rubanova, Y., Bettencourt, J., Duvenaud, D.K.: Neural ordinary differential equations. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31. Curran Associates, Inc. (2018). https:\/\/proceedings.neurips.cc\/paper\/2018\/file\/69386f6bb1dfed68692a24c8686939b9-Paper.pdf"},{"issue":"1","key":"16_CR4","first-page":"2096","volume":"17","author":"Y Ganin","year":"2016","unstructured":"Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17(1), 2096\u20132130 (2016)","journal-title":"J. Mach. Learn. Res."},{"key":"16_CR5","first-page":"1","volume":"27","author":"I Goodfellow","year":"2014","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27, 1\u20139 (2014)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"16_CR6","unstructured":"Grathwohl, W., Chen, R.T.Q., Bettencourt, J., Sutskever, I., Duvenaud, D.: Ffjord: Free-form continuous dynamics for scalable reversible generative models. In: International Conference on Learning Representations (2019)"},{"key":"16_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107423","volume":"106","author":"BB Jia","year":"2020","unstructured":"Jia, B.B., Zhang, M.L.: Multi-dimensional classification via KNN feature augmentation. Pattern Recogn. 106, 107423 (2020)","journal-title":"Pattern Recogn."},{"key":"16_CR8","doi-asserted-by":"crossref","unstructured":"Jia, B.B., Zhang, M.L.: Maximum margin multi-dimensional classification. IEEE Trans. Neural Netw. Learn. Syst. (2021)","DOI":"10.1109\/TNNLS.2021.3084373"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Jia, B.B., Zhang, M.L.: Multi-dimensional classification via sparse label encoding. In: International Conference on Machine Learning, pp. 4917\u20134926. PMLR (2021)","DOI":"10.1109\/TKDE.2021.3100436"},{"key":"16_CR10","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2014)"},{"key":"16_CR11","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: Bengio, Y., LeCun, Y. (eds.) 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, 14\u201316 April 2014, Conference Track Proceedings (2014). http:\/\/arxiv.org\/abs\/1312.6114"},{"key":"16_CR12","first-page":"4743","volume":"29","author":"DP Kingma","year":"2016","unstructured":"Kingma, D.P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., Welling, M.: Improved variational inference with inverse autoregressive flow. Adv. Neural. Inf. Process. Syst. 29, 4743\u20134751 (2016)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"16_CR13","unstructured":"Larsen, A.B.L., S\u00f8nderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. In: International Conference on Machine Learning, pp. 1558\u20131566. PMLR (2016)"},{"key":"16_CR14","first-page":"1","volume":"28","author":"W Liu","year":"2015","unstructured":"Liu, W., Tsang, I.W.: On the optimality of classifier chain for multi-label classification. Adv. Neural Inf. Process. Syst. 28, 1\u20139 (2015)","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"16_CR15","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1016\/j.neucom.2017.09.057","volume":"275","author":"Z Ma","year":"2018","unstructured":"Ma, Z., Chen, S.: Multi-dimensional classification via a metric approach. Neurocomputing 275, 1121\u20131131 (2018)","journal-title":"Neurocomputing"},{"key":"16_CR16","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.patcog.2018.09.005","volume":"86","author":"Z Ma","year":"2019","unstructured":"Ma, Z., Chen, S.: A convex formulation for multiple ordinal output classification. Pattern Recogn. 86, 73\u201384 (2019)","journal-title":"Pattern Recogn."},{"issue":"3","key":"16_CR17","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.1016\/j.patcog.2013.10.006","volume":"47","author":"J Read","year":"2014","unstructured":"Read, J., Martino, L., Luengo, D.: Efficient Monte Carlo methods for multi-dimensional learning with classifier chains. Pattern Recogn. 47(3), 1535\u20131546 (2014)","journal-title":"Pattern Recogn."},{"issue":"3","key":"16_CR18","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","volume":"85","author":"J Read","year":"2011","unstructured":"Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333\u2013359 (2011)","journal-title":"Mach. Learn."},{"key":"16_CR19","unstructured":"Rezende, D., Mohamed, S.: Variational inference with normalizing flows. In: International Conference on Machine Learning, pp. 1530\u20131538. PMLR (2015)"},{"key":"16_CR20","first-page":"3483","volume":"28","author":"K Sohn","year":"2015","unstructured":"Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. Adv. Neural. Inf. Process. Syst. 28, 3483\u20133491 (2015)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"7","key":"16_CR21","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1109\/TKDE.2010.164","volume":"23","author":"G Tsoumakas","year":"2010","unstructured":"Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multilabel classification. IEEE Trans. Knowl. Data Eng. 23(7), 1079\u20131089 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7167\u20137176 (2017)","DOI":"10.1109\/CVPR.2017.316"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Wang, H., Chen, C., Liu, W., Chen, K., Hu, T., Chen, G.: Incorporating label embedding and feature augmentation for multi-dimensional classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 6178\u20136185 (2020)","DOI":"10.1609\/aaai.v34i04.6083"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Yeh, C.K., Wu, W.C., Ko, W.J., Wang, Y.C.F.: Learning deep latent space for multi-label classification. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10769"},{"key":"16_CR25","unstructured":"Zaragoza, J.C., Sucar, E., Morales, E., Bielza, C., Larranaga, P.: Bayesian chain classifiers for multidimensional classification. In: Twenty-Second International Joint Conference on Artificial Intelligence (2011)"},{"issue":"8","key":"16_CR26","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","volume":"26","author":"ML Zhang","year":"2013","unstructured":"Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819\u20131837 (2013)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"16_CR27","doi-asserted-by":"crossref","unstructured":"Zhao, W., Kong, S., Bai, J., Fink, D., Gomes, C.: Hot-VAE: learning high-order label correlation for multi-label classification via attention-based variational autoencoders. arXiv preprint arXiv:2103.06375 (2021)","DOI":"10.1609\/aaai.v35i17.17762"}],"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\/978-3-031-18907-4_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,26]],"date-time":"2022-10-26T19:07:42Z","timestamp":1666811262000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18907-4_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031189067","9783031189074"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18907-4_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 October 2022","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":"Shenzhen","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/en.prcv.cn\/","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":"microsoft","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"564","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":"233","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":"41% - 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":"3.03","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":"3.35","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)"}}]}}