{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T09:51:25Z","timestamp":1770976285334,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":42,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698110","type":"print"},{"value":"9789819698127","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-981-96-9812-7_40","type":"book-chapter","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T07:26:45Z","timestamp":1753428405000},"page":"480-496","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncertainty-Aware Label Regularisation Driven by Class Embedding with Attention Mechanism"],"prefix":"10.1007","author":[{"given":"Han","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiwei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghui","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Ko, J., Yi, B., Yun, S.-Y.: A gift from label smoothing: robust training with adaptive label smoothing via auxiliary classifier under label noise. In: AAAI, vol. 37, pp. 8325\u20138333 (2023)","DOI":"10.1609\/aaai.v37i7.26004"},{"key":"40_CR2","unstructured":"Gong, X., Bisht, N., Xu, G.: Does label smoothing help deep partial label learning? In: Proceedings of the International Conference on Machine Learning, Vienna, Austria, pp. 1\u201316 (2024)"},{"key":"40_CR3","unstructured":"M\u00fcller, R., Kornblith, S., Hinton, G.: When does label smoothing help? In: Proceedings of the Conference on Neural Information Processing Systems, pp. 1\u201310 (2019)"},{"key":"40_CR4","unstructured":"Hinton, G., Dean, J., Vinyals, O.: Distilling the knowledge in a neural network. In: Proceedings of the Conference on Neural Information Processing Systems, pp. 1\u20139 (2014)"},{"key":"40_CR5","doi-asserted-by":"crossref","unstructured":"Liu, B., Ayed, I.B., Galdran, A., Dolz, J.: The devil is in the margin: margin-based label smoothing for network calibration. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 80\u201388. IEEE, New Orleans (2022)","DOI":"10.1109\/CVPR52688.2022.00018"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Jiao, X., et al.: TinyBERT: Distilling BERT for natural language understanding. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 4163\u20134174 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.372"},{"key":"40_CR7","doi-asserted-by":"crossref","unstructured":"Zeng, D., Zhu, J., Chen, H., Dai, J., Jiang, L.: Document-level denoising relation extraction with false-negative mining and reinforced positive-class knowledge distillation. Inf. Process. Manage. 61 (2024)","DOI":"10.1016\/j.ipm.2023.103533"},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, K., Li, T., Liu, B., Liu, Q.: Co-saliency detection via mask-guided fully convolutional networks with multi-scale label smoothing. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, pp. 3090\u20133099 (2019)","DOI":"10.1109\/CVPR.2019.00321"},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Yuan, L., Tay, F.E., Li, G., Wang, T., Feng, J.: Revisiting knowledge distillation via label smoothing regularization. Presented at the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2020)","DOI":"10.1109\/CVPR42600.2020.00396"},{"key":"40_CR10","unstructured":"Vasudeva, S.A., Dolz, J., Lombaert, H.: GeoLS: geodesic label smoothing for image segmentation. In: Medical Imaging with Deep Learning, pp. 468\u2013478 (2024)"},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Sultan, M.A.: Knowledge distillation \u2248 label smoothing: Fact or fallacy? In: Proceedings of the Conference on Neural Information Processing Systems, pp. 4469\u20134477 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.271"},{"key":"40_CR12","doi-asserted-by":"publisher","first-page":"7499","DOI":"10.1109\/TNNLS.2022.3229161","volume":"35","author":"V Borisov","year":"2024","unstructured":"Borisov, V., Leemann, T., Se\u00dfler, K., Haug, J., Pawelczyk, M., Kasneci, G.: Deep neural networks and tabular data: a survey. IEEE Trans. Neural Netw. Learn. Syst. 35, 7499\u20137519 (2024)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"40_CR13","unstructured":"Grinsztajn, L., Oyallon, E., Varoquaux, G.: Why do tree-based models still outperform deep learning on typical tabular data? In: Proceedings of the Conference on Neural Information Processing Systems, New Orleans, LA, USA, pp. 1\u201313 (2022)"},{"key":"40_CR14","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.inffus.2021.11.011","volume":"81","author":"R Shwartz-Ziv","year":"2022","unstructured":"Shwartz-Ziv, R., Armon, A.: Tabular data: deep learning is not all you need. Inf. Fusion 81, 84\u201390 (2022)","journal-title":"Inf. Fusion"},{"key":"40_CR15","doi-asserted-by":"crossref","unstructured":"Liu, H., Qiu, Q., Zhang, Q.: End-to-end approach of multi-grained embedding of categorical features in tabular data. Inf. Process. Manage. 61 (2024)","DOI":"10.1016\/j.ipm.2024.103645"},{"key":"40_CR16","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826. (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"40_CR17","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neunet.2022.09.018","volume":"156","author":"F Gao","year":"2022","unstructured":"Gao, F., Luo, X., Yang, Z., Zhang, Q.: Label smoothing and task-adaptive loss function based on prototype network for few-shot learning. Neural Netw. 156, 39\u201348 (2022)","journal-title":"Neural Netw."},{"key":"40_CR18","doi-asserted-by":"crossref","unstructured":"Gao, Y., et al.: SoftCLIP: softer cross-modal alignment makes CLIP stronger. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 1860\u20131868 (2024)","DOI":"10.1609\/aaai.v38i3.27955"},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"dos Santos, C.F.G., Papa, J.P.: Rethinking regularization with random label smoothing. Neural Process. Lett. 56 (2024)","DOI":"10.1007\/s11063-024-11579-z"},{"key":"40_CR20","doi-asserted-by":"publisher","first-page":"5984","DOI":"10.1109\/TIP.2021.3089942","volume":"30","author":"C-B Zhang","year":"2021","unstructured":"Zhang, C.-B., et al.: Delving deep into label smoothing. IEEE Trans. Image Process. 30, 5984\u20135996 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"40_CR21","unstructured":"Liu, P., Xi, X., Ye, W., Zhang, S.: Label smoothing for text mining. In: Calzolari, N., et al. (eds.) Proceedings of the 29th International Conference on Computational Linguistics, pp. 2210\u20132219. International Committee on Computational Linguistics, Gyeongju (2022)"},{"key":"40_CR22","unstructured":"Bahri, D., Jiang, H.: Locally adaptive label smoothing improves predictive churn. In: Proceedings of the 38th International Conference on Machine Learning, pp. 532\u2013542 (2021)"},{"key":"40_CR23","doi-asserted-by":"crossref","unstructured":"Park, H., Noh, J., Oh, Y., Baek, D., Ham, B.: ACLS: adaptive and conditional label smoothing for network calibration. In: 2023 IEEE\/CVF International Conference on Computer Vision, Paris, France, pp. 3913\u20133922 (2023)","DOI":"10.1109\/ICCV51070.2023.00364"},{"key":"40_CR24","doi-asserted-by":"publisher","first-page":"2076","DOI":"10.1109\/TKDE.2020.3010949","volume":"34","author":"D Liu","year":"2022","unstructured":"Liu, D., Wu, J., Li, J., Du, B., Chang, J., Li, X.: Adaptive hierarchical attention-enhanced gated network integrating reviews for item recommendation. IEEE Trans. Knowl. Data Eng. 34, 2076\u20132090 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"40_CR25","unstructured":"Bachmann, G., Anagnostidis, S., Hofmann, T.: Scaling MLPs: a tale of inductive bias. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"40_CR26","doi-asserted-by":"crossref","unstructured":"Wolf, S., Loran, D., Beyerer, J.: Knowledge-distillation-based label smoothing for fine-grained open set vehicle recognition. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision Workshops, pp. 330\u2013340 (2024)","DOI":"10.1109\/WACVW60836.2024.00041"},{"key":"40_CR27","unstructured":"Zhang, Z., Sabuncu, M.: Self-distillation as instance-specific label smoothing. In: Advances in Neural Information Processing Systems, pp. 2184\u20132195 (2020)"},{"key":"40_CR28","unstructured":"Yuan, H., Shi, Y., Xu, N., Yang, X., Geng, X., Rui, Y.: Learning from biased soft labels. In: Advances in Neural Information Processing Systems, vol. 36 (2024)"},{"key":"40_CR29","unstructured":"Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Machine learning: Proceedings of the thirteenth international conference, Bari, Italy, pp. 148\u2013156 (1996)"},{"key":"40_CR30","doi-asserted-by":"crossref","unstructured":"Chang, J., Lan, Z., Cheng, C., Wei, Y.: Data uncertainty learning in face recognition. In: 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, pp. 5709\u20135718 (2020)","DOI":"10.1109\/CVPR42600.2020.00575"},{"key":"40_CR31","unstructured":"Markelle Kelly, K.N., Longjohn, R.: The UCI machine learning repository. https:\/\/archive.ics.uci.edu"},{"key":"40_CR32","first-page":"255","volume":"17","author":"J Derrac","year":"2015","unstructured":"Derrac, J., Garcia, S., Sanchez, L., Herrera, F.: Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework. J. Mult. Valued Logic Soft Comput. 17, 255\u2013287 (2015)","journal-title":"J. Mult. Valued Logic Soft Comput."},{"key":"40_CR33","doi-asserted-by":"crossref","unstructured":"Lienen, J., H\u00fcllermeier, E.: From label smoothing to label relaxation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 8583\u20138591 (2021)","DOI":"10.1609\/aaai.v35i10.17041"},{"key":"40_CR34","unstructured":"Wei, J., Liu, H., Liu, T., Niu, G., Sugiyama, M., Liu, Y.: To smooth or not? When label smoothing meets noisy labels. In: Proceedings of the 39th International Conference on Machine Learning, pp. 23589\u201323614 (2022)"},{"key":"40_CR35","first-page":"4388","volume":"44","author":"L Zhang","year":"2022","unstructured":"Zhang, L., Bao, C., Ma, K.: Self-distillation: towards efficient and compact neural networks. IEEE Trans. Pattern Anal. Mach. Intell. 44, 4388\u20134403 (2022)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"40_CR36","doi-asserted-by":"crossref","unstructured":"Di, X., Zheng, Y., Liu, X., Cheng, Y.: ProS: facial omni-representation learning via prototype-based self-distillation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 6087\u20136098 (2024)","DOI":"10.1109\/WACV57701.2024.00598"},{"key":"40_CR37","doi-asserted-by":"crossref","unstructured":"Zhan, M., Shi, X., Liu, F., Hu, R.: IGCNN-FC: boosting interpretability and generalization of convolutional neural networks for few chest X-rays analysis. Inf. Process. Manage. 60 (2023)","DOI":"10.1016\/j.ipm.2022.103258"},{"key":"40_CR38","doi-asserted-by":"crossref","unstructured":"Pan, Z., Wu, F., Zhang, B.: Fine-grained image-text matching by cross-modal hard aligning network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, pp. 19275\u201319284 (2023)","DOI":"10.1109\/CVPR52729.2023.01847"},{"key":"40_CR39","doi-asserted-by":"publisher","first-page":"1654","DOI":"10.1109\/TCDS.2021.3131253","volume":"14","author":"J Yu","year":"2022","unstructured":"Yu, J., Gao, H., Chen, Y., Zhou, D., Liu, J., Ju, Z.: Adaptive spatiotemporal representation learning for skeleton-based human action recognition. IEEE Trans. Cogn. Dev.. 14, 1654\u20131665 (2022)","journal-title":"IEEE Trans. Cogn. Dev.."},{"key":"40_CR40","doi-asserted-by":"crossref","unstructured":"Huo, J., Cai, H., Meng, Q.: Independent dual graph attention convolutional network for skeleton-based action recognition. Neurocomputing. 583 (2024)","DOI":"10.1016\/j.neucom.2024.127496"},{"key":"40_CR41","doi-asserted-by":"crossref","unstructured":"Li, X., Gao, W., Feng, S., Wang, D., Joty, S.: Span-level emotion cause analysis by BERT-based graph attention network. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management, Queensland, Australia, pp. 3221\u20133226 (2021)","DOI":"10.1145\/3459637.3482185"},{"key":"40_CR42","doi-asserted-by":"crossref","unstructured":"Yang, H., Chen, L., Pan, S., Wang, H., Zhang, P.: Discrete embedding for attributed graphs. Pattern Recogn. 123 (2022)","DOI":"10.1016\/j.patcog.2021.108368"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9812-7_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T08:57:33Z","timestamp":1770973053000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9812-7_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698110","9789819698127"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9812-7_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","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":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 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":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}