{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T15:55:38Z","timestamp":1784044538762,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T00:00:00Z","timestamp":1544400000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100003629","name":"Korea Meteorological Administration","doi-asserted-by":"publisher","award":["KMI(2017-00410)"],"award-info":[{"award-number":["KMI(2017-00410)"]}],"id":[{"id":"10.13039\/501100003629","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010193","name":"Korea Electric Power Corporation","doi-asserted-by":"publisher","award":["R18XA05"],"award-info":[{"award-number":["R18XA05"]}],"id":[{"id":"10.13039\/501100010193","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Peer-to-Peer Netw. Appl."],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1007\/s12083-018-0702-9","type":"journal-article","created":{"date-parts":[[2018,12,10]],"date-time":"2018-12-10T02:32:47Z","timestamp":1544409167000},"page":"1358-1368","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Naive semi-supervised deep learning using pseudo-label"],"prefix":"10.1007","volume":"12","author":[{"given":"Zhun","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"ByungSoo","family":"Ko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ho-Jin","family":"Choi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,12,10]]},"reference":[{"key":"702_CR1","unstructured":"Ardehaly EM, Culotta A (2017) Co-training for demographic classification using deep learning from label proportions. In: 2017 IEEE international conference on data mining workshops (ICDMW), IEEE, pp 1017\u20131024"},{"key":"702_CR2","unstructured":"Bachman P, Alsharif O, Precup D (2014) Learning with pseudo-ensembles. In: Advances in neural information processing systems, pp 3365\u20133373"},{"key":"702_CR3","doi-asserted-by":"crossref","unstructured":"Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. In: Advances in neural information processing systems, pp 153\u2013160","DOI":"10.7551\/mitpress\/7503.003.0024"},{"issue":"6","key":"702_CR4","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1109\/18.556600","volume":"42","author":"V Castelli","year":"1996","unstructured":"Castelli V, Cover TM (1996) The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter. IEEE Trans Inf Theory 42(6):2102\u20132117","journal-title":"IEEE Trans Inf Theory"},{"key":"702_CR5","doi-asserted-by":"publisher","unstructured":"Chen DD, Wang W, Gao W, Zhou ZH (2018) Tri-net for semi-supervised deep learning. In: Proceedings of the Twenty-Seventh international joint conference on artificial intelligence, IJCAI-18, International Joint Conferences on Artificial Intelligence Organization, pp 2014\u20132020. https:\/\/doi.org\/10.24963\/ijcai.2018\/278","DOI":"10.24963\/ijcai.2018\/278"},{"key":"702_CR6","unstructured":"Cheng Y, Zhao X, Cai R, Li Z, Huang K, Rui Y (2016) Semi-supervised multimodal deep learning for rgb-d object recognition. In: IJCAI, pp 3345\u20133351"},{"key":"702_CR7","unstructured":"Chongxuan L, Xu T, Zhu J, Zhang B (2017) Triple generative adversarial nets. In: Advances in neural information processing systems, pp 4088\u20134098"},{"key":"702_CR8","unstructured":"Dai Z, Yang Z, Yang F, Cohen WW, Salakhutdinov RR (2017) Good semi-supervised learning that requires a bad gan. In: Advances in neural information processing systems, pp 6510\u20136520"},{"issue":"1-3","key":"702_CR9","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1023\/A:1012454411458","volume":"46","author":"D Decoste","year":"2002","unstructured":"Decoste D, Sch\u00f6lkopf B (2002) Training invariant support vector machines. Mach Learn 46(1-3):161\u2013190","journal-title":"Mach Learn"},{"key":"702_CR10","unstructured":"Dosovitskiy A, Springenberg JT, Riedmiller M, Brox T (2014) Discriminative unsupervised feature learning with convolutional neural networks. In: Advances in neural information processing systems, pp 766\u2013774"},{"issue":"1","key":"702_CR11","first-page":"3133","volume":"15","author":"M Fern\u00e1ndez-Delgado","year":"2014","unstructured":"Fern\u00e1ndez-Delgado M, Cernadas E, Barro S, Amorim D (2014) Do we need hundreds of classifiers to solve real world classification problems? J Mach Learn Res 15(1):3133\u20133181","journal-title":"J Mach Learn Res"},{"key":"702_CR12","doi-asserted-by":"publisher","unstructured":"Grandvalet Y, Bengio Y (2006) Entropy regularization. Semi-supervised learning, pp 151\u2013168. https:\/\/doi.org\/10.7551\/mitpress\/9780262033589.001.0001","DOI":"10.7551\/mitpress\/9780262033589.001.0001"},{"issue":"5786","key":"702_CR13","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1126\/science.1127647","volume":"313","author":"GE Hinton","year":"2006","unstructured":"Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504\u2013 507","journal-title":"Science"},{"issue":"8","key":"702_CR14","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"702_CR15","doi-asserted-by":"crossref","unstructured":"Jarrett K, Kavukcuoglu K, LeCun Y et al (2009) What is the best multi-stage architecture for object recognition?. In: 2009 IEEE 12th international conference on Computer vision, IEEE, pp 2146\u20132153","DOI":"10.1109\/ICCV.2009.5459469"},{"key":"702_CR16","unstructured":"Kingma DP, Mohamed S, Rezende DJ, Welling M (2014) Semi-supervised learning with deep generative models. In: Advances in neural information processing systems, pp 3581\u20133589"},{"key":"702_CR17","unstructured":"Krizhevsky A, Hinton G. (2009) Learning multiple layers of features from tiny images"},{"key":"702_CR18","unstructured":"Laine S, Aila T (2016) Temporal ensembling for semi-supervised learning. arXiv: 1610.02242"},{"issue":"11","key":"702_CR19","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"key":"702_CR20","unstructured":"LeCun Y, Cortes C, Burges C.J. (1998) The mnist database of handwritten digits"},{"key":"702_CR21","unstructured":"Lee DH (2013) Pseudo-label: the simple and efficient semi-supervised learning method for deep neural networks. In: Work shop on challenges in representation learning, ICML, vol 3, p 2"},{"key":"702_CR22","unstructured":"Luo Y, Zhu J, Li M, Ren Y, Zhang B (2017) Smooth neighbors on teacher graphs for semi-supervised learning. arXiv: 1711.00258"},{"key":"702_CR23","unstructured":"Maal\u00f8e L., S\u00f8nderby C.K., S\u00f8nderby S.K., Winther O. (2016) Auxiliary deep generative models. In: International conference on machine learning, pp 1445\u20131453"},{"key":"702_CR24","unstructured":"Miyato T, Maeda S.I., Koyama M., Ishii S. (2017) Virtual adversarial training: a regularization method for supervised and semi-supervised learning. arXiv: 1704.03976"},{"key":"702_CR25","unstructured":"Odena A (2016) Semi-supervised learning with generative adversarial networks. arXiv: 1606.01583"},{"issue":"364","key":"702_CR26","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1080\/01621459.1978.10480106","volume":"73","author":"TJ O\u2019neill","year":"1978","unstructured":"O\u2019neill TJ (1978) Normal discrimination with unclassified observations. J Am Stat Assoc 73(364):821\u2013826","journal-title":"J Am Stat Assoc"},{"key":"702_CR27","unstructured":"Park S, Park JK, Shin SJ, Moon IC (2017) Adversarial dropout for supervised and semi-supervised learning. arXiv: 1707.03631"},{"key":"702_CR28","unstructured":"Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv: 1511.06434"},{"key":"702_CR29","doi-asserted-by":"crossref","unstructured":"Ranzato M, Szummer M (2008) Semi-supervised learning of compact document representations with deep networks. In: Proceedings of the 25th international conference on machine learning, ACM, pp 792\u2013799","DOI":"10.1145\/1390156.1390256"},{"key":"702_CR30","unstructured":"Rasmus A, Berglund M, Honkala M, Valpola H, Raiko T (2015) Semi-supervised learning with ladder networks. In: Advances in neural information processing systems, pp 3546\u20133554"},{"key":"702_CR31","unstructured":"Rolnick D, Veit A, Belongie S, Shavit N (2017) Deep learning is robust to massive label noise. arXiv: 1705.10694"},{"issue":"3","key":"702_CR32","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","volume":"115","author":"O Russakovsky","year":"2015","unstructured":"Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115(3):211\u2013252","journal-title":"Int J Comput Vis"},{"key":"702_CR33","doi-asserted-by":"crossref","unstructured":"Sajjadi M, Javanmardi M, Tasdizen T (2016) Mutual exclusivity loss for semi-supervised deep learning. In: 2016 IEEE international conference on image processing (ICIP), IEEE, pp 1908\u20131912","DOI":"10.1109\/ICIP.2016.7532690"},{"key":"702_CR34","unstructured":"Sajjadi M, Javanmardi M, tasdizen T (2016) Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In: Advances in neural information processing systems, pp 1163\u20131171"},{"key":"702_CR35","unstructured":"Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. In: Advances in neural information processing systems, pp 2234\u20132242"},{"key":"702_CR36","unstructured":"Springenberg JT (2015) Unsupervised and semi-supervised learning with categorical generative adversarial networks. arXiv: 1511.06390"},{"key":"702_CR37","unstructured":"Wan L, Zeiler M, Zhang S, Le Cun Y, Fergus R (2013) Regularization of neural networks using dropconnect. In: International conference on machine learning, pp 1058\u20131066"},{"key":"702_CR38","unstructured":"Weston J, Ratle F, Mobahi H, Collobert R (2012) Deep learning via semi-supervised embedding. In: Neural networks: tricks of the trade, Springer, pp 639\u2013655"},{"key":"702_CR39","doi-asserted-by":"crossref","unstructured":"Yan Y, Xu Z, Tsang IW, Long G, Yang Y (2016) Robust semi-supervised learning through label aggregation. In: AAAI, pp 2244\u20132250","DOI":"10.1609\/aaai.v30i1.10276"},{"key":"702_CR40","unstructured":"Zhao JJ, Mathieu M, Goroshin R, LeCun Y (2015) Stacked what-where auto-encoders. CoRR arXiv: abs\/1506.02351"},{"issue":"3","key":"702_CR41","first-page":"4","volume":"2","author":"X Zhu","year":"2006","unstructured":"Zhu X (2006) Semi-supervised learning literature survey. Computer Science University of Wisconsin-Madison 2(3):4","journal-title":"Computer Science University of Wisconsin-Madison"},{"issue":"1","key":"702_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2200\/S00196ED1V01Y200906AIM006","volume":"3","author":"X Zhu","year":"2009","unstructured":"Zhu X, Goldberg AB (2009) Introduction to semi-supervised learning. Synthesis lectures on artificial intelligence and machine learning 3(1):1\u2013130","journal-title":"Synthesis lectures on artificial intelligence and machine learning"}],"container-title":["Peer-to-Peer Networking and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-018-0702-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s12083-018-0702-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s12083-018-0702-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,11]],"date-time":"2023-09-11T23:40:41Z","timestamp":1694475641000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s12083-018-0702-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,10]]},"references-count":42,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2019,9]]}},"alternative-id":["702"],"URL":"https:\/\/doi.org\/10.1007\/s12083-018-0702-9","relation":{},"ISSN":["1936-6442","1936-6450"],"issn-type":[{"value":"1936-6442","type":"print"},{"value":"1936-6450","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,10]]},"assertion":[{"value":"19 February 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 November 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}