{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T16:43:56Z","timestamp":1777567436977,"version":"3.51.4"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031729423","type":"print"},{"value":"9783031729430","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"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\/978-3-031-72943-0_16","type":"book-chapter","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T13:39:37Z","timestamp":1732801177000},"page":"275-290","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Neural Spectral Decomposition for\u00a0Dataset Distillation"],"prefix":"10.1007","author":[{"given":"Shaolei","family":"Yang","sequence":"first","affiliation":[]},{"given":"Shen","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Mingbo","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Haoqiang","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Xing","family":"Wei","sequence":"additional","affiliation":[]},{"given":"Shuaicheng","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Belouadah, E., Popescu, A.: ScaIL: classifier weights scaling for class incremental learning. In: WACV, pp. 1266\u20131275 (2020)","DOI":"10.1109\/WACV45572.2020.9093562"},{"key":"16_CR2","doi-asserted-by":"crossref","unstructured":"Cazenavette, G., Wang, T., Torralba, A., Efros, A.A., Zhu, J.Y.: Dataset distillation by matching training trajectories. In: CVPR, pp. 4750\u20134759 (2022)","DOI":"10.1109\/CVPR52688.2022.01045"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Cazenavette, G., Wang, T., Torralba, A., Efros, A.A., Zhu, J.Y.: Generalizing dataset distillation via deep generative prior. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3739\u20133748 (2023)","DOI":"10.1109\/CVPR52729.2023.00364"},{"key":"16_CR4","unstructured":"Chen, Y., Welling, M., Smola, A.: Super-samples from kernel herding. arXiv:1203.3472 (2012)"},{"key":"16_CR5","doi-asserted-by":"crossref","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. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"16_CR6","first-page":"34391","volume":"35","author":"Z Deng","year":"2022","unstructured":"Deng, Z., Russakovsky, O.: Remember the past: distilling datasets into addressable memories for neural networks. Adv. Neural. Inf. Process. Syst. 35, 34391\u201334404 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"Du, J., Jiang, Y., Tan, V.Y., Zhou, J.T., Li, H.: Minimizing the accumulated trajectory error to improve dataset distillation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3749\u20133758 (2023)","DOI":"10.1109\/CVPR52729.2023.00365"},{"key":"16_CR8","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s11023-020-09548-1","volume":"30","author":"L Floridi","year":"2020","unstructured":"Floridi, L., Chiriatti, M.: GPT-3: its nature, scope, limits, and consequences. Mind. Mach. 30, 681\u2013694 (2020)","journal-title":"Mind. Mach."},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4367\u20134375 (2018)","DOI":"10.1109\/CVPR.2018.00459"},{"key":"16_CR10","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)","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Huang, Z., Zhang, Z., Lan, C., Zha, Z.J., Lu, Y., Guo, B.: Adaptive frequency filters as efficient global token mixers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6049\u20136059 (2023)","DOI":"10.1109\/ICCV51070.2023.00556"},{"key":"16_CR12","unstructured":"Kim, J.H., et al.: Dataset condensation via efficient synthetic-data parameterization. arXiv:2205.14959 (2022)"},{"key":"16_CR13","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"16_CR14","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images (2009)"},{"key":"16_CR15","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25 (2012)"},{"key":"16_CR16","unstructured":"Le, Y., Yang, X.: Tiny imageNet visual recognition challenge. CS 231N 7(7), 3 (2015)"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Li, H., et al.: Single-image-based deep learning for segmentation of early esophageal cancer lesions. IEEE Trans. Image Process. (2024)","DOI":"10.1109\/TIP.2024.3379902"},{"key":"16_CR18","first-page":"1100","volume":"35","author":"S Liu","year":"2022","unstructured":"Liu, S., Wang, K., Yang, X., Ye, J., Wang, X.: Dataset distillation via factorization. Adv. Neural. Inf. Process. Syst. 35, 1100\u20131113 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"16_CR19","unstructured":"Ma, Z., Cao, A., Yang, F., Wei, X.: Curriculum dataset distillation. arXiv preprint arXiv:2405.09150 (2024)"},{"key":"16_CR20","doi-asserted-by":"publisher","unstructured":"Ma, Z., Gao, D., Yang, S., Wei, X., Gong, Y.: Dataset condensation via expert subspace projection. Sensors 23(19) (2023). https:\/\/doi.org\/10.3390\/s23198148, https:\/\/www.mdpi.com\/1424-8220\/23\/19\/8148","DOI":"10.3390\/s23198148"},{"key":"16_CR21","unstructured":"Nguyen, T., Novak, R., Xiao, L., Lee, J.: Dataset distillation with infinitely wide convolutional networks. NeurIPS, vol. 34, pp. 5186\u20135198 (2021)"},{"key":"16_CR22","unstructured":"Oquab, M., et\u00a0al.: DINOv2: learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)"},{"key":"16_CR23","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"16_CR24","unstructured":"Toneva, M., Sordoni, A., Combes, R.T.D., Trischler, A., Bengio, Y., Gordon, G.J.: An empirical study of example forgetting during deep neural network learning. arXiv preprint arXiv:1812.05159 (2018)"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Wang, K., et l.: CAFE: learning to condense dataset by aligning features. In: CVPR, pp. 12196\u201312205 (2022)","DOI":"10.1109\/CVPR52688.2022.01188"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"Wang, Q., et al.: Tracking everything everywhere all at once. arXiv preprint arXiv:2306.05422 (2023)","DOI":"10.1109\/ICCV51070.2023.01813"},{"key":"16_CR27","unstructured":"Wang, T., et al: Caption anything: interactive image description with diverse multimodal controls. arXiv preprint arXiv:2305.02677 (2023)"},{"key":"16_CR28","unstructured":"Wang, T., Zhu, J.Y., Torralba, A., Efros, A.A.: Dataset distillation. arXiv preprint arXiv:1811.10959 (2018)"},{"key":"16_CR29","unstructured":"Wei, X., Cao, A., Yang, F., Ma, Z.: Sparse parameterization for epitomic dataset distillation. In: Oh, A., Naumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, 10\u201316 December 2023, New Orleans, LA, USA (2023). http:\/\/papers.nips.cc\/paper_files\/paper\/2023\/hash\/9e8889198d16fb79926e71adbe38cae4-Abstract-Conference.html"},{"key":"16_CR30","doi-asserted-by":"crossref","unstructured":"Xu, K., Qin, M., Sun, F., Wang, Y., Chen, Y.K., Ren, F.: Learning in the frequency domain. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1740\u20131749 (2020)","DOI":"10.1109\/CVPR42600.2020.00181"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Xu, K., Yang, X., Yin, B., Lau, R.W.: Learning to restore low-light images via decomposition-and-enhancement. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2281\u20132290 (2020)","DOI":"10.1109\/CVPR42600.2020.00235"},{"key":"16_CR32","unstructured":"Zhao, B., Bilen, H.: Dataset condensation with differentiable Siamese augmentation. In: ICML, pp. 12674\u201312685. PMLR (2021)"},{"key":"16_CR33","unstructured":"Zhao, B., Bilen, H.: Dataset condensation with distribution matching. arXiv:2110.04181 (2021)"},{"key":"16_CR34","unstructured":"Zhao, B., Mopuri, K.R., Bilen, H.: Dataset condensation with gradient matching. In: ICLR, vol. 1, no. 2, p. 3 (2021)"},{"key":"16_CR35","first-page":"9813","volume":"35","author":"Y Zhou","year":"2022","unstructured":"Zhou, Y., Nezhadarya, E., Ba, J.: Dataset distillation using neural feature regression. Adv. Neural. Inf. Process. Syst. 35, 9813\u20139827 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-72943-0_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T14:19:44Z","timestamp":1732803584000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72943-0_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"ISBN":["9783031729423","9783031729430"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72943-0_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"29 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}