{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:44:04Z","timestamp":1743090244426,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":41,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819772438"},{"type":"electronic","value":"9789819772445"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-97-7244-5_4","type":"book-chapter","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T17:02:38Z","timestamp":1724778158000},"page":"46-60","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Noisy Label Learning Based on\u00a0Weighted Neighborhood Consistency"],"prefix":"10.1007","author":[{"given":"Qian","family":"Rong","sequence":"first","affiliation":[]},{"given":"Lu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ling","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Xuanang","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Guohui","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"unstructured":"Arazo, E., Ortego, D., Albert, P., O\u2019Connor, N., McGuinness, K.: Unsupervised label noise modeling and loss correction. In: International Conference on Machine Learning, pp. 312\u2013321. PMLR (2019)","key":"4_CR1"},{"unstructured":"Arpit, D., et al.: A closer look at memorization in deep networks. In: International Conference on Machine Learning, pp. 233\u2013242. PMLR (2017)","key":"4_CR2"},{"unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: a holistic approach to semi-supervised learning. In: Advances in Neural Information Processing Systems, vol. 32 (2019)","key":"4_CR3"},{"issue":"4","key":"4_CR4","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1145\/792538.792543","volume":"50","author":"A Blum","year":"2003","unstructured":"Blum, A., Kalai, A., Wasserman, H.: Noise-tolerant learning, the parity problem, and the statistical query model. J. ACM (JACM) 50(4), 506\u2013519 (2003)","journal-title":"J. ACM (JACM)"},{"doi-asserted-by":"crossref","unstructured":"Chen, X., Gupta, A.: Webly supervised learning of convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1431\u20131439 (2015)","key":"4_CR5","DOI":"10.1109\/ICCV.2015.168"},{"doi-asserted-by":"crossref","unstructured":"Cheng, D., et al.: Instance-dependent label-noise learning with manifold-regularized transition matrix estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16630\u201316639 (2022)","key":"4_CR6","DOI":"10.1109\/CVPR52688.2022.01613"},{"unstructured":"Cordeiro, F.R., Belagiannis, V., Reid, I., Carneiro, G.: Propmix: hard sample filtering and proportional mixup for learning with noisy labels. arXiv preprint arXiv:2110.11809 (2021)","key":"4_CR7"},{"key":"4_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109013","volume":"133","author":"FR Cordeiro","year":"2023","unstructured":"Cordeiro, F.R., Sachdeva, R., Belagiannis, V., Reid, I., Carneiro, G.: Longremix: robust learning with high confidence samples in a noisy label environment. Pattern Recogn. 133, 109013 (2023)","journal-title":"Pattern Recogn."},{"issue":"10","key":"4_CR9","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1145\/2347736.2347755","volume":"55","author":"P Domingos","year":"2012","unstructured":"Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78\u201387 (2012)","journal-title":"Commun. ACM"},{"doi-asserted-by":"crossref","unstructured":"Ghosh, A., Kumar, H., Sastry, P.S.: Robust loss functions under label noise for deep neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a031 (2017)","key":"4_CR10","DOI":"10.1609\/aaai.v31i1.10894"},{"unstructured":"Goldberger, J., Ben-Reuven, E.: Training deep neural-networks using a noise adaptation layer. In: International Conference on Learning Representations (2016)","key":"4_CR11"},{"unstructured":"Goldberger, J., Ben-Reuven, E.: Training deep neural-networks using a noise adaptation layer. In: International Conference on Learning Representations (2017)","key":"4_CR12"},{"unstructured":"Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. In: Advances in Neural Information Processing Systems, vol. 31 (2018)","key":"4_CR13"},{"doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","key":"4_CR14","DOI":"10.1109\/CVPR.2016.90"},{"unstructured":"Hendrycks, D., Mazeika, M., Wilson, D., Gimpel, K.: Using trusted data to train deep networks on labels corrupted by severe noise. In: Advances in Neural Information Processing Systems, vol. 31 (2018)","key":"4_CR15"},{"doi-asserted-by":"crossref","unstructured":"Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018)","key":"4_CR16","DOI":"10.18653\/v1\/P18-1031"},{"doi-asserted-by":"crossref","unstructured":"Iscen, A., Valmadre, J., Arnab, A., Schmid, C.: Learning with neighbor consistency for noisy labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4672\u20134681 (2022)","key":"4_CR17","DOI":"10.1109\/CVPR52688.2022.00463"},{"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":"4_CR18"},{"unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)","key":"4_CR19"},{"unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25 (2012)","key":"4_CR20"},{"unstructured":"Li, J., Socher, R., Hoi, S.C.: Dividemix: learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394 (2020)","key":"4_CR21"},{"unstructured":"Liu, S., Zhu, Z., Qu, Q., You, C.: Robust training under label noise by over-parameterization. In: International Conference on Machine Learning, pp. 14153\u201314172. PMLR (2022)","key":"4_CR22"},{"unstructured":"Liu, Y., Cheng, H., Zhang, K.: Identifiability of label noise transition matrix. In: International Conference on Machine Learning, pp. 21475\u201321496. PMLR (2023)","key":"4_CR23"},{"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":"4_CR24"},{"key":"4_CR25","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s10791-017-9321-y","volume":"21","author":"KD Onal","year":"2018","unstructured":"Onal, K.D., et al.: Neural information retrieval: at the end of the early years. Inf. Retrieval J. 21, 111\u2013182 (2018)","journal-title":"Inf. Retrieval J."},{"key":"4_CR26","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"735","DOI":"10.1007\/978-3-031-30637-2_49","volume-title":"DASFAA 2023","author":"Q Rong","year":"2023","unstructured":"Rong, Q., Yuan, L., Li, G., Li, J., Zhang, L., Ding, X.: A static bi-dimensional sample selection for federated learning with label noise. In: Wang, X., et al. (eds.) DASFAA 2023. LNCS, vol. 13943, pp. 735\u2013744. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-30637-2_49"},{"doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., Ben-David, S.: Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, Cambridge (2014)","key":"4_CR27","DOI":"10.1017\/CBO9781107298019"},{"doi-asserted-by":"crossref","unstructured":"Smart, B., Carneiro, G.: Bootstrapping the relationship between images and their clean and noisy labels. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 5344\u20135354 (2023)","key":"4_CR28","DOI":"10.1109\/WACV56688.2023.00531"},{"key":"4_CR29","doi-asserted-by":"publisher","first-page":"1093","DOI":"10.1109\/TMM.2021.3116430","volume":"24","author":"Z Sun","year":"2021","unstructured":"Sun, Z., Liu, H., Wang, Q., Zhou, T., Wu, Q., Tang, Z.: Co-LDL: a co-training-based label distribution learning method for tackling label noise. IEEE Trans. Multimedia 24, 1093\u20131104 (2021)","journal-title":"IEEE Trans. Multimedia"},{"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)","key":"4_CR30","DOI":"10.1145\/3474085.3475622"},{"unstructured":"Wang, H., Xiao, R., Dong, Y., Feng, L., Zhao, J.: Promix: combating label noise via maximizing clean sample utility. arXiv preprint arXiv:2207.10276 (2022)","key":"4_CR31"},{"doi-asserted-by":"crossref","unstructured":"Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., Bailey, J.: Symmetric cross entropy for robust learning with noisy labels. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 322\u2013330 (2019)","key":"4_CR32","DOI":"10.1109\/ICCV.2019.00041"},{"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)","key":"4_CR33","DOI":"10.1109\/CVPR42600.2020.01374"},{"unstructured":"Wei, H., et al.: Logit clipping for robust learning against label noise. arXiv preprint arXiv:2212.04055 (2022)","key":"4_CR34"},{"unstructured":"Wei, J., Zhu, Z., Cheng, H., Liu, T., Niu, G., Liu, Y.: Learning with noisy labels revisited: a study using real-world human annotations. In: International Conference on Learning Representations (2022). https:\/\/openreview.net\/forum?id=TBWA6PLJZQm","key":"4_CR35"},{"unstructured":"Xia, X., et al.: Are anchor points really indispensable in label-noise learning? In: Advances in Neural Information Processing Systems, vol. 32 (2019)","key":"4_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":"4_CR37"},{"doi-asserted-by":"crossref","unstructured":"Yi, K., Wu, J.: Probabilistic end-to-end noise correction for learning with noisy labels. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7017\u20137025 (2019)","key":"4_CR38","DOI":"10.1109\/CVPR.2019.00718"},{"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":"4_CR39"},{"issue":"3","key":"4_CR40","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"},{"unstructured":"Zhang, Y., Niu, G., Sugiyama, M.: Learning noise transition matrix from only noisy labels via total variation regularization. In: International Conference on Machine Learning, pp. 12501\u201312512. PMLR (2021)","key":"4_CR41"}],"container-title":["Lecture Notes in Computer Science","Web and Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-7244-5_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T17:04:31Z","timestamp":1724778271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-7244-5_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819772438","9789819772445"],"references-count":41,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-7244-5_4","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"28 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"APWeb-WAIM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jinhua","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 September 2024","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":"apwebwaim2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/apweb2024.zjnu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}