{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T01:32:43Z","timestamp":1778808763005,"version":"3.51.4"},"publisher-location":"Cham","reference-count":53,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031726699","type":"print"},{"value":"9783031726705","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-72670-5_22","type":"book-chapter","created":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T07:01:50Z","timestamp":1727593310000},"page":"388-404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Rebalancing Using Estimated Class Distribution for\u00a0Imbalanced Semi-supervised Learning Under Class Distribution Mismatch"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7235-3231","authenticated-orcid":false,"given":"Taemin","family":"Park","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2221-7820","authenticated-orcid":false,"given":"Hyuck","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6415-9887","authenticated-orcid":false,"given":"Heeyoung","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"22_CR1","unstructured":"Berthelot, D., Carlini, N., an\u00a0Alex\u00a0Kurakin, E.D.C., Sohn, K., Zhang, H., Raffel, C.: Remixmatch: Semi-supervised learning with distribution matching and augmentation anchoring. In: ICLR (2020)"},{"key":"22_CR2","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.: Mixmatch: a holistic approach to semi-supervised learning. In: NeurIPS (2019)"},{"key":"22_CR3","unstructured":"Cao, K., Wei, C., Gaidon, A., Arechiga, N., Ma, T.: Learning imbalanced datasets with label-distribution-aware margin loss. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019 Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a032. Curran Associates, Inc. (2019)"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: Synthetic minority over-sampling technique. J. Artif. Intell. Res., 321\u2013357 (2002)","DOI":"10.1613\/jair.953"},{"key":"22_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1007\/978-3-030-58526-6_41","volume-title":"Computer Vision \u2013 ECCV 2020","author":"P Chu","year":"2020","unstructured":"Chu, P., Bian, X., Liu, S., Ling, H.: Feature space augmentation for long-tailed data. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12374, pp. 694\u2013710. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58526-6_41"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.: Randaugment: practical automated data augmentation with a reduced search space. In: NeurIPS (2020)","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Large scale fine-grained categorization and domain-specific transfer learning. In: CVPR, pp. 4109\u20134118 (2018)","DOI":"10.1109\/CVPR.2018.00432"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Cui, Y., Jia, M., Lin, T.Y., Song, Y., Belongie, S.: Class-balanced loss based on effective number of samples. In: CVPR, pp. 9268\u20139277 (2019)","DOI":"10.1109\/CVPR.2019.00949"},{"key":"22_CR9","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"issue":"1","key":"22_CR10","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1111\/j.0824-7935.2004.t01-1-00228.x","volume":"20","author":"A Estabrooks","year":"2004","unstructured":"Estabrooks, A., Jo, T., Japkowicz, N.: A multiple resampling method for learning from imbalanced data sets. Comput. Intell. 20(1), 18\u201336 (2004)","journal-title":"Comput. Intell."},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Fan, Y., Dai, D., Kukleva, A., Schiele, B.: Cossl: Co-learning of representation and classifier for imbalanced semi-supervised learning. In: CVPR, pp. 14574\u201314584 (2022)","DOI":"10.1109\/CVPR52688.2022.01417"},{"key":"22_CR12","unstructured":"Gidaris, S., Singh, P., Komodakis, N.: Unsupervised representation learning by predicting image rotations. In: ICLR (2018)"},{"key":"22_CR13","unstructured":"Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: NeurIPS (2004)"},{"key":"22_CR14","unstructured":"Guo, L.Z., Li, Y.F.: Class-imbalanced semi-supervised learning with adaptive thresholding. In: ICML, vol.\u00a0162, pp. 8082\u20138094 (2022)"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Huang, C., Li, Y., Loy, C.C., Tang, X.: Learning deep representation for imbalanced classification. In: CVPR, pp. 5375\u20135384 (2016)","DOI":"10.1109\/CVPR.2016.580"},{"key":"22_CR16","unstructured":"Japkowicz, N.: The class imbalance problem: Significance and strategies. In: Proc. 2000 International Conference on Artificial Intelligence, vol.\u00a01, pp. 111\u2013117 (2000)"},{"key":"22_CR17","unstructured":"Japkowicz, N.: The class imbalance problem: Significance and strategies. In: Proceedings of the International Conference on Artificial Intelligence (2000)"},{"key":"22_CR18","unstructured":"Kang, B., Li, Y., Xie, S., Yuan, Z., Feng, J.: Exploring balanced feature spaces for representation learning. In: ICLR (2021)"},{"key":"22_CR19","unstructured":"Kang, B., et al.: Decoupling representation and classifier for long-tailed recognition. In: ICLR (2020)"},{"key":"22_CR20","unstructured":"Kim, J., Hur, Y., Park, S., Yang, E., Hwang, S., Shin, J.: Distribution aligning refinery of pseudo-label for imbalanced semi-supervised learning. In: NeurIPS (2020)"},{"key":"22_CR21","unstructured":"Krizhevsky, A.: Learning multiple layers of features from tiny images. Technical report, Department of Computer Science, University of Toronto (2009)"},{"key":"22_CR22","unstructured":"Kubat, M., Matwin, S., et\u00a0al.: Addressing the curse of imbalanced training sets: one-sided selection. In: ICML, vol.\u00a097, p.\u00a0179. Citeseer (1997)"},{"key":"22_CR23","unstructured":"Lai, Z., Wang, C., Gunawan, H., Cheung, S.C., Chuah, C.N.: Smoothed adaptive weighting for imbalanced semi-supervised learning: Improve reliability against unknown distribution data. In: ICML, vol.\u00a0162, pp. 11828\u201311843 (2022)"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Lazarow, J., Sohn, K., Lee, C.Y., Li, C.L., Zhang, Z., Pfister, T.: Unifying distribution alignment as a loss for imbalanced semi-supervised learning. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 5644\u20135653 (2023)","DOI":"10.1109\/WACV56688.2023.00560"},{"key":"22_CR25","unstructured":"Lee, D.H.: Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: In Workshop on challenges in representation learning (ICML) (2013)"},{"key":"22_CR26","doi-asserted-by":"crossref","unstructured":"Lee, H., Kim, H.: Cdmad: class-distribution-mismatch-aware debiasing for class-imbalanced semi-supervised learning. In: CVPR, pp. 23891\u201323900 (2024)","DOI":"10.1109\/CVPR52733.2024.02255"},{"key":"22_CR27","unstructured":"Lee, H., Shin, S., Kim, H.: Abc: auxiliary balanced classifier for class-imbalanced semi-supervised learning. In: NeurIPS (2021)"},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Li, J., Meng, Z., Shi, D., Song, R., Diao, X., Wang, J., , Xu, H.: FCC: feature clusters compression for long-tailed visual recognition. In: CVPR, pp. 24080\u201324089 (2023)","DOI":"10.1109\/CVPR52729.2023.02306"},{"key":"22_CR29","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.neucom.2013.05.051","volume":"128","author":"K Li","year":"2014","unstructured":"Li, K., Kong, X., Lu, Z., Wenyin, L., Yin, J.: Boosting weighted elm for imbalanced learning. Neurocomputing 128, 15\u201321 (2014)","journal-title":"Neurocomputing"},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(2), 539\u2013550 (2008)","DOI":"10.1109\/TSMCB.2008.2007853"},{"key":"22_CR31","doi-asserted-by":"crossref","unstructured":"Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., Yu., S.X.: Large-scale long-tailed recognition in an open world. In: CVPR, pp. 2537\u20132546 (2019)","DOI":"10.1109\/CVPR.2019.00264"},{"key":"22_CR32","unstructured":"Menon, A.K., Jayasumana, S., Rawat, A.S., Jain, H., Veit, A., Kumar, S.: Long-tail learning via logit adjustment. In: ICLR (2021)"},{"key":"22_CR33","doi-asserted-by":"crossref","unstructured":"Miyato, T., ichi Maeda, S., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979\u20131993 (2018)","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"22_CR34","doi-asserted-by":"crossref","unstructured":"Oh, Y., Kim, D.J., Kweon, I.S.: Daso: distribution-aware semantics-oriented pseudo-label for imbalanced semi-supervised learning. In: CVPR, pp. 9786\u20139796 (2022)","DOI":"10.1109\/CVPR52688.2022.00956"},{"key":"22_CR35","unstructured":"Ren, J., et al.: Balanced meta-softmax for long-tailed visual recognition. In: NeurIPS (2020)"},{"key":"22_CR36","unstructured":"Ren, M., Zeng, W., Yang, B., Urtasun, R.: Learning to reweight examples for robust deep learning. In: ICML, vol.\u00a080, pp. 4334\u20134343 (2018)"},{"key":"22_CR37","unstructured":"Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In: NeurIPS (2016)"},{"key":"22_CR38","unstructured":"Sohn, K., et al.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. In: NeurIPS (2020)"},{"key":"22_CR39","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.neunet.2021.10.008","volume":"145","author":"V Verma","year":"2022","unstructured":"Verma, V., Kawaguchi, K., Lamb, A., Kannala, J., Solin, A., Bengio, Y., Lopez-Paz, D.: Interpolation consistency training for semi-supervised learning. Neural Netw. 145, 90\u2013106 (2022)","journal-title":"Neural Netw."},{"key":"22_CR40","unstructured":"Wang, R., Jia, X., Wang, Q., Wu, Y., Meng, D.: Imbalanced semi-supervised learning with bias adaptive classifier. In: ICLR (2023)"},{"key":"22_CR41","doi-asserted-by":"crossref","unstructured":"Wang, S., Minku, L.L., Yao, X.: A learning framework for online class imbalance learning. In: IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL), pp. 36\u201345 (2013)","DOI":"10.1109\/CIEL.2013.6613138"},{"issue":"5","key":"22_CR42","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1109\/TKDE.2014.2345380","volume":"27","author":"S Wang","year":"2015","unstructured":"Wang, S., Minku, L.L., Yao, X.: Resampling-based ensemble methods for online class imbalance learning. IEEE Trans. Knowl. Data Eng. 27(5), 1356\u20131368 (2015)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"22_CR43","unstructured":"Wang, X., Lian, L., Miao, Z., Liu, Z., Yu., S.X.: Long-tailed recognition by routing diverse distribution-aware experts. In: ICLR (2021)"},{"key":"22_CR44","doi-asserted-by":"crossref","unstructured":"Wang, X., Wu, Z., Lian, L., Yu, S.X.: Debiased learning from naturally imbalanced pseudo-labels. In: CVPR, pp. 14647\u201314657 (2022)","DOI":"10.1109\/CVPR52688.2022.01424"},{"key":"22_CR45","unstructured":"Wang, Y., et al.: Freematch: self-adaptive thresholding for semi-supervised learning. In: ICLR (2023)"},{"key":"22_CR46","doi-asserted-by":"crossref","unstructured":"Wei, C., Sohn, K., Mellina, C., Yuille, A., Yang, F.: Crest: a class-rebalancing self-training framework for imbalanced semi-supervised learning. In: CVPR, pp. 10857\u201310866 (2021)","DOI":"10.1109\/CVPR46437.2021.01071"},{"key":"22_CR47","doi-asserted-by":"crossref","unstructured":"Wei, T., Gan, K.: Towards realistic long-tailed semi-supervised learning: consistency is all you need. In: CVPR, pp. 3469\u20133478 (2023)","DOI":"10.1109\/CVPR52729.2023.00338"},{"key":"22_CR48","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/978-3-030-58558-7_15","volume-title":"Computer Vision \u2013 ECCV 2020","author":"L Xiang","year":"2020","unstructured":"Xiang, L., Ding, G., Han, J.: Learning from multiple experts: self-paced knowledge distillation for long-tailed classification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 247\u2013263. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_15"},{"key":"22_CR49","unstructured":"Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., Xiao, J.: Lsun: construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015)"},{"key":"22_CR50","unstructured":"Yu, Z., Li, Y., Lee, Y.J.: Inpl: Pseudo-labeling the inliers first for imbalanced semi-supervised learning. In: ICLR (2023)"},{"key":"22_CR51","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: beyond empirical risk minimization. In: ICLR (2018)"},{"key":"22_CR52","unstructured":"Zhang, Y., Hooi, B., Hong, L., Feng, J.: Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition. In: NeurIPS (2022)"},{"key":"22_CR53","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Cui, J., Liu, S., Jia, J.: Improving calibration for long-tailed recognition. In: CVPR, pp. 16489\u201316498 (2021)","DOI":"10.1109\/CVPR46437.2021.01622"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72670-5_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T07:23:43Z","timestamp":1727594623000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72670-5_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,30]]},"ISBN":["9783031726699","9783031726705"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72670-5_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,30]]},"assertion":[{"value":"30 September 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}