{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T09:23:48Z","timestamp":1743067428025,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811607042"},{"type":"electronic","value":"9789811607059"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-0705-9_6","type":"book-chapter","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T13:03:53Z","timestamp":1617195833000},"page":"83-95","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Small-Scale Dataset Classification Performance Through Weak-Label Samples Generated by InfoGAN"],"prefix":"10.1007","author":[{"given":"Meiyang","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Qiguang","family":"Miao","sequence":"additional","affiliation":[]},{"given":"Daohui","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Zili","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,1]]},"reference":[{"issue":"1","key":"6_CR1","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1109\/TIP.2017.2756450","volume":"27","author":"G Hu","year":"2017","unstructured":"Hu, G., Peng, X., Yang, Y., Hospedales, T.M., Verbeek, J.: Frankenstein: learning deep face representations using small data. IEEE Trans. Image Process. 27(1), 293\u2013303 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR2","doi-asserted-by":"crossref","unstructured":"Chen, M., Shi, X., Zhang, Y., Wu, D., Guizani, M.: Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Trans. Big Data (2017)","DOI":"10.1109\/TBDATA.2017.2717439"},{"key":"6_CR3","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4353\u20134361 (2015)","DOI":"10.1109\/CVPR.2015.7299064"},{"key":"6_CR4","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"6_CR5","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN. arXiv preprint arXiv:1701.07875 (2017)"},{"key":"6_CR6","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"6_CR7","doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","DOI":"10.1109\/CVPR.2019.00453"},{"key":"6_CR8","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2172\u20132180 (2016)"},{"key":"6_CR9","unstructured":"Odena, A.: Semi-supervised learning with generative adversarial networks. arXiv preprint arXiv:1606.01583 (2016)"},{"key":"6_CR10","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, pp. 2234\u20132242 (2016)"},{"key":"6_CR11","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3754\u20133762 (2017)","DOI":"10.1109\/ICCV.2017.405"},{"issue":"3","key":"6_CR12","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1109\/TIP.2018.2874715","volume":"28","author":"Y Huang","year":"2018","unstructured":"Huang, Y., Xu, J., Wu, Q., Zheng, Z., Zhang, Z., Zhang, J.: Multi-pseudo regularized label for generated data in person re-identification. IEEE Trans. Image Process. 28(3), 1391\u20131403 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"6_CR13","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1007\/978-3-030-29551-6_38","volume-title":"Knowledge Science, Engineering and Management","author":"M Zhang","year":"2019","unstructured":"Zhang, M., Zhang, Z.: Small-scale data classification based on deep forest. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds.) KSEM 2019. LNCS (LNAI), vol. 11775, pp. 428\u2013439. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-29551-6_38"},{"key":"6_CR14","unstructured":"Zhou, Z.H., Feng, J.: Deep forest. arXiv preprint arXiv:1702.08835 (2017)"},{"key":"6_CR15","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536\u20132544 (2016)","DOI":"10.1109\/CVPR.2016.278"},{"key":"6_CR16","unstructured":"Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. arXiv preprint arXiv:1605.05396 (2016)"},{"key":"6_CR17","unstructured":"Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: Advances in Neural Information Processing Systems, pp. 82\u201390 (2016)"},{"key":"6_CR18","doi-asserted-by":"crossref","unstructured":"Wen, H., Zhang, J., Lin, Q., Yang, K., Huang, P.: Multi-level deep cascade trees for conversion rate prediction in recommendation system. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 338\u2013345 (2019)","DOI":"10.1609\/aaai.v33i01.3301338"},{"issue":"4","key":"6_CR19","doi-asserted-by":"publisher","first-page":"2955","DOI":"10.1007\/s00500-019-04073-5","volume":"24","author":"H Wang","year":"2019","unstructured":"Wang, H., Tang, Y., Jia, Z., Ye, F.: Dense adaptive cascade forest: a self-adaptive deep ensemble for classification problems. Soft Comput. 24(4), 2955\u20132968 (2019). https:\/\/doi.org\/10.1007\/s00500-019-04073-5","journal-title":"Soft Comput."},{"key":"6_CR20","doi-asserted-by":"crossref","unstructured":"Pang, M., Ting, K.M., Zhao, P., Zhou, Z.H.: Improving deep forest by confidence screening. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 1194\u20131199. IEEE (2018)","DOI":"10.1109\/ICDM.2018.00158"},{"issue":"5","key":"6_CR21","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1186\/s12859-018-2095-4","volume":"19","author":"Y Guo","year":"2018","unstructured":"Guo, Y., Liu, S., Li, Z., Shang, X.: BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data. BMC Bioinform. 19(5), 118 (2018)","journal-title":"BMC Bioinform."}],"container-title":["Communications in Computer and Information Science","Big Data"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-0705-9_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T13:05:16Z","timestamp":1617195916000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-981-16-0705-9_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811607042","9789811607059"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-0705-9_6","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BigData","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF Conference on Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chongqing","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bigdat2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/bigdata2020.swu.edu.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":"CCF online submission system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65","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":"16","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":"25% - 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","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}