{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T14:10:00Z","timestamp":1767449400602,"version":"3.48.0"},"publisher-location":"Singapore","reference-count":42,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819534524"},{"type":"electronic","value":"9789819534531"}],"license":[{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T00:00:00Z","timestamp":1760572800000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-3453-1_19","type":"book-chapter","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T08:00:47Z","timestamp":1760515247000},"page":"281-295","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Noise-Robust Learning via\u00a0Full Consistency"],"prefix":"10.1007","author":[{"given":"Zhen","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueying","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenxin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenlong","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohui","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongfeng","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,16]]},"reference":[{"key":"19_CR1","unstructured":"Arpit, D., et al.: A closer look at memorization in deep networks. arXiv preprint arXiv:1706.05394 (2017)"},{"key":"19_CR2","doi-asserted-by":"crossref","unstructured":"Bai, Y., Liu, T.: Me-momentum: extracting hard confident examples from noisily labeled data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9312\u20139321 (2021)","DOI":"10.1109\/ICCV48922.2021.00918"},{"key":"19_CR3","first-page":"24392","volume":"34","author":"Y Bai","year":"2021","unstructured":"Bai, Y., et al.: Understanding and improving early stopping for learning with noisy labels. Adv. Neural. Inf. Process. Syst. 34, 24392\u201324403 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR4","unstructured":"Ben-Shaul, I., Shwartz-Ziv, R., Galanti, T., Dekel, S., LeCun, Y.: Reverse engineering self-supervised learning. arXiv preprint arXiv:2305.15614 (2023)"},{"key":"19_CR5","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: a holistic approach to semi-supervised learning. Adv. Neural Inform. Process. Syst. 32 (2019)"},{"key":"19_CR6","unstructured":"Chen, J., et al.: Label-retrieval-augmented diffusion models for learning from noisy labels. Adv. Neural Inform. Process. Syst. 36 (2024)"},{"key":"19_CR7","unstructured":"Han, B., et al.: Sigua: forgetting may make learning with noisy labels more robust. In: International Conference on Machine Learning, pp. 4006\u20134016. PMLR (2020)"},{"key":"19_CR8","unstructured":"Han, B., et al.: A survey of label-noise representation learning: Past, present and future. arXiv preprint arXiv:2011.04406 (2020)"},{"key":"19_CR9","unstructured":"Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. Adv. Neural Inform. Process. Syst. 31 (2018)"},{"key":"19_CR10","unstructured":"Hu, W., Li, Z., Yu, D.: Simple and effective regularization methods for training on noisily labeled data with generalization guarantee. arXiv preprint arXiv:1905.11368 (2019)"},{"key":"19_CR11","unstructured":"Huang, Z., et al.: Harnessing out-of-distribution examples via augmenting content and style. arXiv preprint arXiv:2207.03162 (2022)"},{"key":"19_CR12","unstructured":"Jiang, L., Zhou, Z., Leung, T., Li, L.J., Fei-Fei, L.: Mentornet: learning data-driven curriculum for very deep neural networks on corrupted labels. In: International Conference on Machine Learning, pp. 2304\u20132313. PMLR (2018)"},{"key":"19_CR13","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"key":"19_CR14","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (NeurIPS), vol.\u00a025, pp. 1097\u20131105. Curran Associates, Inc. (2012)"},{"key":"19_CR15","unstructured":"Li, J., Hoi, S.C.H., Socher, R.: Towards noise-robust contrastive learning. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)"},{"key":"19_CR16","unstructured":"Li, J., Socher, R., Hoi, S.C.: Dividemix: Learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394 (2020)"},{"key":"19_CR17","unstructured":"Li, M., Soltanolkotabi, M., Oymak, S.: Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks. In: International Conference on Artificial Intelligence and Statistics, pp. 4313\u20134324. PMLR (2020)"},{"key":"19_CR18","doi-asserted-by":"crossref","unstructured":"Li, Y., Yang, J., Song, Y., Cao, L., Luo, J., Li, L.J.: Learning from noisy labels with distillation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1910\u20131918 (2017)","DOI":"10.1109\/ICCV.2017.211"},{"key":"19_CR19","unstructured":"Liu, Y., Guo, H.: Peer loss functions: learning from noisy labels without knowing noise rates. In: International Conference on Machine Learning, pp. 6226\u20136236. PMLR (2020)"},{"key":"19_CR20","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011)"},{"key":"19_CR21","unstructured":"Nguyen, D.T., Mummadi, C.K., Ngo, T.P.N., Nguyen, T.H.P., Beggel, L., Brox, T.: Self: learning to filter noisy labels with self-ensembling. arXiv preprint arXiv:1910.01842 (2019)"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Patel, D., Sastry, P.: Adaptive sample selection for robust learning under label noise. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3932\u20133942 (2023)","DOI":"10.1109\/WACV56688.2023.00392"},{"key":"19_CR23","first-page":"17044","volume":"33","author":"G Pleiss","year":"2020","unstructured":"Pleiss, G., Zhang, T., Elenberg, E., Weinberger, K.Q.: Identifying mislabeled data using the area under the margin ranking. Adv. Neural. Inf. Process. Syst. 33, 17044\u201317056 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR24","unstructured":"Rolnick, D., Veit, A., Belongie, S., Shavit, N.: Deep learning is robust to massive label noise. arXiv preprint arXiv:1705.10694 (2017)"},{"key":"19_CR25","first-page":"596","volume":"33","author":"K Sohn","year":"2020","unstructured":"Sohn, K., et al.: Fixmatch: simplifying semi-supervised learning with consistency and confidence. Adv. Neural. Inf. Process. Syst. 33, 596\u2013608 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR26","doi-asserted-by":"crossref","unstructured":"Song, H., Kim, M., Park, D., Shin, Y., Lee, J.G.: Learning from noisy labels with deep neural networks: a survey. IEEE Trans. Neural Netw. Learn. Syst. (2022)","DOI":"10.1109\/TNNLS.2022.3152527"},{"issue":"1","key":"19_CR27","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Swayamdipta, S., et al.: Dataset cartography: Mapping and diagnosing datasets with training dynamics. arXiv preprint arXiv:2009.10795 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.746"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Tan, C., Xia, J., Wu, L., Li, S.Z.: Co-learning: learning from noisy labels with self-supervision. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 1405\u20131413 (2021)","DOI":"10.1145\/3474085.3475622"},{"key":"19_CR30","doi-asserted-by":"crossref","unstructured":"Tanaka, D., Ikami, D., Yamasaki, T., Aizawa, K.: Joint optimization framework for learning with noisy labels. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5552\u20135560 (2018)","DOI":"10.1109\/CVPR.2018.00582"},{"key":"19_CR31","unstructured":"Toneva, M., Sordoni, A., Tsvetkov, Y., Jaakkola, T., Bengio, G.: An empirical study of example forgetting during deep neural network learning. In: International Conference on Learning Representations (ICLR) (2019)"},{"key":"19_CR32","doi-asserted-by":"crossref","unstructured":"Wei, H., Feng, L., Chen, X., An, B.: Combating noisy labels by agreement: a joint training method with co-regularization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13726\u201313735 (2020)","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"19_CR33","doi-asserted-by":"crossref","unstructured":"Xia, X., Han, B., Zhan, Y., Yu, J., Gong, M., Gong, C., Liu, T.: Combating noisy labels with sample selection by mining high-discrepancy examples. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1833\u20131843 (2023)","DOI":"10.1109\/ICCV51070.2023.00176"},{"key":"19_CR34","unstructured":"Xia, X., et al.: Sample selection with uncertainty of losses for learning with noisy labels. arXiv preprint arXiv:2106.00445 (2021)"},{"key":"19_CR35","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)"},{"key":"19_CR36","unstructured":"Xiao, T., Xia, T., Yang, Y., Huang, C., Wang, X.: Learning from massive noisy labeled data for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2691\u20132699 (2015)"},{"key":"19_CR37","first-page":"6256","volume":"33","author":"Q Xie","year":"2020","unstructured":"Xie, Q., Dai, Z., Hovy, E., Luong, T., Le, Q.: Unsupervised data augmentation for consistency training. Adv. Neural. Inf. Process. Syst. 33, 6256\u20136268 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"19_CR38","doi-asserted-by":"crossref","unstructured":"Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imagenet classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)","DOI":"10.1109\/CVPR42600.2020.01070"},{"key":"19_CR39","doi-asserted-by":"crossref","unstructured":"Yang, X., Song, Z., King, I., Xu, Z.: A survey on deep semi-supervised learning. IEEE Trans. Knowl. Data Eng. (2022)","DOI":"10.1109\/TKDE.2022.3220219"},{"key":"19_CR40","unstructured":"Yu, X., Han, B., Yao, J., Niu, G., Tsang, I., Sugiyama, M.: How does disagreement help generalization against label corruption? In: International Conference on Machine Learning, pp. 7164\u20137173. PMLR (2019)"},{"key":"19_CR41","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. In: International Conference on Learning Representations (ICLR) (2017)"},{"issue":"3","key":"19_CR42","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1145\/3446776","volume":"64","author":"C Zhang","year":"2021","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning (still) requires rethinking generalization. Commun. ACM 64(3), 107\u2013115 (2021)","journal-title":"Commun. ACM"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-3453-1_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T14:06:51Z","timestamp":1767449211000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-3453-1_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,16]]},"ISBN":["9789819534524","9789819534531"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-3453-1_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,16]]},"assertion":[{"value":"16 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2025.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}