{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T06:55:56Z","timestamp":1782716156046,"version":"3.54.5"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031975639","type":"print"},{"value":"9783031975646","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\/978-3-031-97564-6_23","type":"book-chapter","created":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T11:23:05Z","timestamp":1751714585000},"page":"293-306","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dataset Distillation via\u00a0Kantorovich-Rubinstein Dual of\u00a0Wasserstein Distance"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0901-2991","authenticated-orcid":false,"given":"Muyang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayu","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yong","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,6]]},"reference":[{"key":"23_CR1","doi-asserted-by":"crossref","unstructured":"Cazenavette, G., Wang, T., Torralba, A., Efros, A.A., Zhu, J.Y.: Dataset distillation by matching training trajectories. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 4750\u20134759, June 2022","DOI":"10.1109\/CVPR52688.2022.01045"},{"issue":"4","key":"23_CR2","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/BF02551274","volume":"2","author":"G Cybenko","year":"1989","unstructured":"Cybenko, G.: Approximation by superpositions of a sigmoidal function. Math. Control Sig. Syst. 2(4), 303\u2013314 (1989)","journal-title":"Math. Control Sig. Syst."},{"key":"23_CR3","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"23_CR4","unstructured":"Feng, Y., Vedantam, R., Kempe, J.: Embarrassingly simple dataset distillation. In: 12th International Conference on Learning Representations, ICLR 2024 (2024)"},{"key":"23_CR5","doi-asserted-by":"crossref","unstructured":"Geng, J., et al.: A survey on dataset distillation: approaches, applications and future directions (2023). https:\/\/arxiv.org\/abs\/2305.01975","DOI":"10.24963\/ijcai.2023\/741"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Guo, C., Zhao, B., Bai, Y.: DeepCore: a comprehensive library for coreset selection in deep learning. In: Strauss, C., Cuzzocrea, A., Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) Database and Expert Systems Applications, pp. 181\u2013195. Springer International Publishing, Cham (2022)","DOI":"10.1007\/978-3-031-12423-5_14"},{"key":"23_CR7","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"issue":"11","key":"23_CR8","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998). https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proc. IEEE"},{"key":"23_CR9","doi-asserted-by":"crossref","unstructured":"Lee, H., Kim, S., Lee, J., Yoo, J., Kwak, N.: Coreset selection for object detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 7682\u20137691, June 2024","DOI":"10.1109\/CVPRW63382.2024.00764"},{"issue":"1","key":"23_CR10","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TPAMI.2023.3322540","volume":"46","author":"S Lei","year":"2024","unstructured":"Lei, S., Tao, D.: A comprehensive survey of dataset distillation. IEEE Trans. Pattern Anal. Mach. Intell. 46(1), 17\u201332 (2024). https:\/\/doi.org\/10.1109\/TPAMI.2023.3322540","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"23_CR11","unstructured":"Liu, H., et al.: Dataset distillation via the Wasserstein metric. arXiv preprint arXiv:2311.18531 (2023)"},{"key":"23_CR12","unstructured":"Netzer, Y., et\u00a0al.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning, vol.\u00a02011, p.\u00a04. Granada (2011)"},{"key":"23_CR13","unstructured":"Nguyen, T., Chen, Z., Lee, J.: Dataset meta-learning from kernel ridge-regression. In: International Conference on Learning Representations (2021)"},{"key":"23_CR14","unstructured":"Nguyen, T., Novak, R., Xiao, L., Lee, J.: Dataset distillation with infinitely wide convolutional networks. In: Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems, vol.\u00a034, pp. 5186\u20135198. Curran Associates, Inc. (2021). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2021\/file\/299a23a2291e2126b91d54f3601ec162-Paper.pdf"},{"key":"23_CR15","unstructured":"Sachdeva, N., McAuley, J.: Data distillation: a survey (2023). https:\/\/arxiv.org\/abs\/2301.04272"},{"key":"23_CR16","unstructured":"Saxe, A.M., Koh, P.W., Chen, Z., Bhand, M., Suresh, B., Ng, A.Y.: On random weights and unsupervised feature learning. In: Proceedings of the 28th International Conference on International Conference on Machine Learning, ICML 2011, pp. 1089\u20131096. Omnipress, Madison, WI, USA (2011)"},{"key":"23_CR17","doi-asserted-by":"crossref","unstructured":"Schuhmann, C., et\u00a0al.: LAION-5B: an open large-scale dataset for training next generation image-text models. In: Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. (eds.) Advances in Neural Information Processing Systems, vol.\u00a035, pp. 25278\u201325294. Curran Associates, Inc. (2022). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2022\/file\/a1859debfb3b59d094f3504d5ebb6c25-Paper-Datasets_and_Benchmarks.pdf","DOI":"10.52202\/068431-1833"},{"key":"23_CR18","unstructured":"Villani, C., et\u00a0al.: Optimal Transport: Old and New, vol.\u00a0338. Springer (2008)"},{"key":"23_CR19","doi-asserted-by":"crossref","unstructured":"Wang, K., et al.: CAFE: learning to condense dataset by aligning features. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12196\u201312205, June 2022","DOI":"10.1109\/CVPR52688.2022.01188"},{"key":"23_CR20","unstructured":"Wang, T., Zhu, J.Y., Torralba, A., Efros, A.A.: Dataset distillation (2020). https:\/\/arxiv.org\/abs\/1811.10959"},{"key":"23_CR21","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)"},{"issue":"8","key":"23_CR22","first-page":"9314","volume":"38","author":"H Zhang","year":"2024","unstructured":"Zhang, H., Li, S., Wang, P., Zeng, D., Ge, S.: M3D: dataset condensation by minimizing maximum mean discrepancy. Proc. AAAI Conf. Artif. Intell. 38(8), 9314\u20139322 (2024)","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"23_CR23","unstructured":"Zhao, B., Bilen, H.: Dataset condensation with differentiable Siamese augmentation. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, 18\u201324 July 2021, vol.\u00a0139, pp. 12674\u201312685. PMLR (2021). https:\/\/proceedings.mlr.press\/v139\/zhao21a.html"},{"key":"23_CR24","doi-asserted-by":"crossref","unstructured":"Zhao, B., Bilen, H.: Dataset condensation with distribution matching. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), January 2023, pp. 6514\u20136523 (2023)","DOI":"10.1109\/WACV56688.2023.00645"},{"key":"23_CR25","unstructured":"Zhao, B., Mopuri, K.R., Bilen, H.: Dataset condensation with gradient matching (2021). https:\/\/arxiv.org\/abs\/2006.05929"},{"key":"23_CR26","doi-asserted-by":"crossref","unstructured":"Zhao, G., Li, G., Qin, Y., Yu, Y.: Improved distribution matching for dataset condensation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023, pp. 7856\u20137865 (2023)","DOI":"10.1109\/CVPR52729.2023.00759"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2025 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-97564-6_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T15:36:53Z","timestamp":1778081813000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-97564-6_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031975639","9783031975646"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-97564-6_23","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":"6 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","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":"7 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}