{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:40:54Z","timestamp":1780317654563,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789811692468","type":"print"},{"value":"9789811692475","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-981-16-9247-5_9","type":"book-chapter","created":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T21:25:33Z","timestamp":1641936333000},"page":"113-126","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DICE: Dynamically Induced Cross Entropy for Robust Learning with Noisy Labels"],"prefix":"10.1007","author":[{"given":"Tianyu","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,1,11]]},"reference":[{"issue":"4","key":"9_CR1","first-page":"343","volume":"2","author":"D Angluin","year":"1988","unstructured":"Angluin, D., Laird, P.: Learning from noisy examples. Mach. Learn. 2(4), 343\u2013370 (1988)","journal-title":"Mach. Learn."},{"key":"9_CR2","unstructured":"Berthon, A., Han, B., Niu, G., Liu, T., Sugiyama, M.: Confidence scores make instance-dependent label-noise learning possible. In: International Conference on Machine Learning, pp. 825\u2013836. PMLR (2021)"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Feng, L., Shu, S., Lin, Z., Lv, F., Li, L., An, B.: Can cross entropy loss be robust to label noise? In: IJCAI, pp. 2206\u20132212 (2020)","DOI":"10.24963\/ijcai.2020\/305"},{"issue":"6","key":"9_CR4","doi-asserted-by":"publisher","first-page":"2825","DOI":"10.1109\/TIP.2017.2689998","volume":"26","author":"BB Gao","year":"2017","unstructured":"Gao, B.B., Xing, C., Xie, C.W., Wu, J., Geng, X.: Deep label distribution learning with label ambiguity. IEEE Trans. Image Process. 26(6), 2825\u20132838 (2017)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Ghosh, A., Kumar, H., Sastry, P.: Robust loss functions under label noise for deep neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)","DOI":"10.1609\/aaai.v31i1.10894"},{"key":"9_CR6","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.neucom.2014.09.081","volume":"160","author":"A Ghosh","year":"2015","unstructured":"Ghosh, A., Manwani, N., Sastry, P.: Making risk minimization tolerant to label noise. Neurocomputing 160, 93\u2013107 (2015)","journal-title":"Neurocomputing"},{"key":"9_CR7","unstructured":"Goldberger, J., Ben-Reuven, E.: Training deep neural-networks using a noise adaptation layer (2016)"},{"key":"9_CR8","unstructured":"Han, B., et al.: Co-teaching: robust training of deep neural networks with extremely noisy labels. arXiv preprint arXiv:1804.06872 (2018)"},{"key":"9_CR9","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":"9_CR10","unstructured":"Menon, A.K., Rawat, A.S., Reddi, S.J., Kumar, S.: Can gradient clipping mitigate label noise? In: International Conference on Learning Representations (2019)"},{"key":"9_CR11","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)"},{"key":"9_CR12","first-page":"1196","volume":"26","author":"N Natarajan","year":"2013","unstructured":"Natarajan, N., Dhillon, I.S., Ravikumar, P.K., Tewari, A.: Learning with noisy labels. Adv. Neural. Inf. Process. Syst. 26, 1196\u20131204 (2013)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., Qu, L.: Making deep neural networks robust to label noise: a loss correction approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1944\u20131952 (2017)","DOI":"10.1109\/CVPR.2017.240"},{"key":"9_CR14","unstructured":"Sukhbaatar, S., Fergus, R.: Learning from noisy labels with deep neural networks. arXiv preprint arXiv:1406.2080 (2014). vol. 2(3), p. 4"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 843\u2013852 (2017)","DOI":"10.1109\/ICCV.2017.97"},{"key":"9_CR16","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"9_CR17","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)","DOI":"10.1109\/ICCV.2019.00041"},{"key":"9_CR18","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":"9_CR19","first-page":"6838","volume":"32","author":"X Xia","year":"2019","unstructured":"Xia, X., et al.: Are anchor points really indispensable in label-noise learning? Adv. Neural. Inf. Process. Syst. 32, 6838\u20136849 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"9_CR20","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)"},{"issue":"4","key":"9_CR21","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1109\/TIP.2018.2877939","volume":"28","author":"J Yao","year":"2018","unstructured":"Yao, J., et al.: Deep learning from noisy image labels with quality embedding. IEEE Trans. Image Process. 28(4), 1909\u20131922 (2018)","journal-title":"IEEE Trans. Image Process."},{"key":"9_CR22","unstructured":"Yao, Q., Yang, H., Han, B., Niu, G., Kwok, J.: Searching to exploit memorization effect in learning from corrupted labels. arXiv preprint arXiv:1911.02377 (2019)"},{"key":"9_CR23","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)","DOI":"10.1109\/CVPR.2019.00718"},{"key":"9_CR24","unstructured":"Zhang, Z., Sabuncu, M.R.: Generalized cross entropy loss for training deep neural networks with noisy labels. In: 32nd Conference on Neural Information Processing Systems (NeurIPS) (2018)"}],"container-title":["Communications in Computer and Information Science","Cognitive Systems and Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-9247-5_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,22]],"date-time":"2023-01-22T16:54:30Z","timestamp":1674406470000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-9247-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9789811692468","9789811692475"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-9247-5_9","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"11 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Cognitive Systems and Signal Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Suzhou","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsip2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iccsip2021.tsingzhan.com\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"105","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"41","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}