{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T08:37:40Z","timestamp":1770885460217,"version":"3.50.1"},"reference-count":12,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2024,1,1]]},"DOI":"10.1587\/transinf.2023mul0002","type":"journal-article","created":{"date-parts":[[2023,12,31]],"date-time":"2023-12-31T22:39:20Z","timestamp":1704062360000},"page":"44-48","source":"Crossref","is-referenced-by-count":6,"title":["Unbiased Pseudo-Labeling for Learning with Noisy Labels"],"prefix":"10.1587","volume":"E107.D","author":[{"given":"Ryota","family":"HIGASHIMOTO","sequence":"first","affiliation":[{"name":"Faculty of Engineering Science, Kansai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soh","family":"YOSHIDA","sequence":"additional","affiliation":[{"name":"Faculty of Engineering Science, Kansai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takashi","family":"HORIHATA","sequence":"additional","affiliation":[{"name":"Faculty of Engineering Science, Kansai University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mitsuji","family":"MUNEYASU","sequence":"additional","affiliation":[{"name":"Faculty of Engineering Science, Kansai University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, \u201cUnderstanding deep learning requires rethinking generalization,\u201d Proc. Int. Conf. Learn. Represent., pp.1-15, 2017."},{"key":"2","unstructured":"[2] D. Arpit, S. Jastrzundefinedbski, N. Ballas, D. Krueger, E. Bengio, M.S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio, and S. Lacoste-Julien, \u201cA closer look at memorization in deep networks,\u201d Proc. Int. Conf. Machin. Learn., pp.233-242, 2017."},{"key":"3","unstructured":"[3] B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, and M. Sugiyama, \u201cCo-teaching: Robust training of deep neural networks with extremely noisy labels,\u201d Advan. Neu. Inf. Proc. Syst., pp.8536-8546, 2018."},{"key":"4","unstructured":"[4] D. Berthelot, N. Carlini, I. Goodfellow, N. Papernot, A. Oliver, and C.A. Raffel, \u201cMixMatch: A holistic approach to semi-supervised learning,\u201d Advan. Neu. Inf. Proc. Syst., pp.5049-5059, 2019."},{"key":"5","unstructured":"[5] K. Sohn, D. Berthelot, N. Carlini, Z. Zhang, H. Zhang, C.A. Raffel, E.D. Cubuk, A. Kurakin, and C.L. Li, \u201cFixMatch: Simplifying semi-supervised learning with consistency and confidence,\u201d Advan. Neu. Inf. Proc. Syst., pp.596-608, 2020."},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] X. Wang, Z. Wu, L. Lian, and S.X. Yu, \u201cDebiased learning from naturally imbalanced pseudo-labels,\u201d Proc. IEEE Conf. Comput. Vis. Pattern Recog., pp.14647-14657, 2022. 10.1109\/cvpr52688.2022.01424","DOI":"10.1109\/CVPR52688.2022.01424"},{"key":"7","unstructured":"[7] J. Li, R. Socher, and S.C. Hoi, \u201cDivideMix: Learning with noisy labels as semi-supervised learning,\u201d Proc. Int. Conf. Learn. Represent., 2020."},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] Y. Nomura and T. Kurita, \u201cConsistency regularization on clean samples for learning with noisy labels,\u201d IEICE Trans. Inf. & Syst., vol.E105-D, no.2, pp.387-395, 2022. 10.1587\/transinf.2021EDP7127","DOI":"10.1587\/transinf.2021EDP7127"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] S. Greenland, J. Pearl, and J.M. Robins, \u201cConfounding and collapsibility in causal inference,\u201d Stat. Sci., vol.14, no.1, pp.29-46, 1999. 10.1214\/ss\/1009211805","DOI":"10.1214\/ss\/1009211805"},{"key":"10","unstructured":"[10] J. Pearl, \u201cDirect and indirect effects,\u201d arXiv preprint arXiv: 1301. 2300, 2013."},{"key":"11","unstructured":"[11] A. Krizhevsky and G. Hinton, \u201cLearning multiple layers of features from tiny images,\u201d Master's thesis, Department of Computer Science, University of Toronto, 2009."},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] K. He, X. Zhang, S. Ren, and J. Sun, \u201cIdentity mappings in deep residual networks,\u201d Euro. Conf. Comput. Vis., vol.9908, pp.630-645, 2016. 10.1007\/978-3-319-46493-0_38","DOI":"10.1007\/978-3-319-46493-0_38"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/1\/E107.D_2023MUL0002\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,6]],"date-time":"2024-01-06T04:15:01Z","timestamp":1704514501000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/1\/E107.D_2023MUL0002\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,1]]},"references-count":12,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2023mul0002","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,1]]},"article-number":"2023MUL0002"}}