{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:07:22Z","timestamp":1760710042085,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030336066"},{"type":"electronic","value":"9783030336073"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-33607-3_35","type":"book-chapter","created":{"date-parts":[[2019,11,7]],"date-time":"2019-11-07T00:05:28Z","timestamp":1573085128000},"page":"319-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Conditioned Generative Model via Latent Semantic Controlling for Learning Deep Representation of Data"],"prefix":"10.1007","author":[{"given":"Jin-Young","family":"Kim","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung-Bae","family":"Cho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,18]]},"reference":[{"key":"35_CR1","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"35_CR2","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"issue":"4","key":"35_CR3","doi-asserted-by":"publisher","first-page":"739","DOI":"10.3390\/en12040739","volume":"12","author":"JY Kim","year":"2019","unstructured":"Kim, J.Y., Cho, S.B.: Electric energy consumption prediction by deep learning with state explainable autoencoder. Energies 12(4), 739\u2013754 (2019)","journal-title":"Energies"},{"key":"35_CR4","unstructured":"Yang, Z., Hu, Z., Salakgutdinov, R., Berg-Kirkpatrick, T.: Improved variational autoencoders for text modeling using dilated convolutions. In: International Conference on Machine Learning, pp. 3881\u20133890 (2017)"},{"key":"35_CR5","unstructured":"Kusner, M.J., Paige, B., Hernandez-Lobato, J.M.: Grammar variational autoencoder. In: International Conference on Machine Learning, pp. 1945\u20131954 (2017)"},{"key":"35_CR6","unstructured":"Denton, E.L., Chintala, S., Fergus, R.: Deep generative image models using a laplacian pyramid of adversarial networks. In: Neural Information Processing Systems, pp. 1486\u20131494 (2015)"},{"key":"35_CR7","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"35_CR8","unstructured":"Zhao, J., Mathieu, M., LeCun, Y.: Energy-based generative adversarial network. arXiv preprint arXiv:1609.03126 (2016)"},{"key":"35_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/978-3-030-03493-1_52","volume-title":"Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2018","author":"J-Y Kim","year":"2018","unstructured":"Kim, J.-Y., Cho, S.-B.: Detecting intrusive malware with a hybrid generative deep learning model. In: Yin, H., Camacho, D., Novais, P., Tall\u00f3n-Ballesteros, Antonio J. (eds.) IDEAL 2018. LNCS, vol. 11314, pp. 499\u2013507. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-03493-1_52"},{"key":"35_CR10","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.ins.2018.04.092","volume":"460","author":"JY Kim","year":"2018","unstructured":"Kim, J.Y., Bu, S.J., Cho, S.B.: Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders. Inf. Sci. 460, 83\u2013102 (2018)","journal-title":"Inf. Sci."},{"key":"35_CR11","unstructured":"Sohn, K., Lee, H., Yan, X.: Learning structured output representation using deep conditional generative models. In: Neural Information Processing Systems, pp. 3483\u20133491 (2015)"},{"key":"35_CR12","unstructured":"Van den Oord, A., Vinyals, O.: Neural discrete representation learning. In: Neural Information Processing Systems, pp. 6309\u20136318 (2017)"},{"key":"35_CR13","unstructured":"Larsen, A.B.L., Sonderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity mertic. arXiv preprint arXiv:1512.09300 (2015)"},{"key":"35_CR14","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schuman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Neural Information Processing Systems, pp. 2172\u20132180 (2016)"},{"issue":"5","key":"35_CR15","first-page":"3510","volume":"2","author":"A Nguyen","year":"2017","unstructured":"Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., Yosinski, J.: Plug & play generative networks: conditional iterative generative of images in latent space. Comput. Vis. Pattern Recogn. 2(5), 3510\u20133520 (2017)","journal-title":"Comput. Vis. Pattern Recogn."},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Smolley, S.P.: Least squares generative adversarial networks. In: International Conference on Computer Vision, pp. 2813\u20132821 (2017)","DOI":"10.1109\/ICCV.2017.304"},{"key":"35_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: International Conference on Computer Vision, pp. 2730\u20132738 (2015)","DOI":"10.1109\/ICCV.2015.425"},{"key":"35_CR18","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 \u2013 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":"35_CR19","unstructured":"Kim, T., Cha, M., Kim, H., Lee, J., Kim, J.: Learning to discover cross-domain relations with generative adversarial networks. In: International Conference on Machine Learning, pp. 1857\u20131865 (2017)"},{"key":"35_CR20","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint arXiv:1703.10593 (2017)","DOI":"10.1109\/ICCV.2017.244"},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"Choi, Y., Choi, M., Kim, M., Ha, J., Kim, S., Choo, J.: StarGAN: unifired generative adversarial networks for multi-domain image-to-image translation. In: Computer Vision and Pattern Recognition, pp. 8789\u20138797 (2018)","DOI":"10.1109\/CVPR.2018.00916"}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33607-3_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T13:58:25Z","timestamp":1710251905000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-33607-3_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030336066","9783030336073"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33607-3_35","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"18 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.confercare.manchester.ac.uk\/events\/ideal2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Open","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":"149","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":"94","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":"63% - 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":"2.5","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","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)"}}]}}