{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T09:22:06Z","timestamp":1754558526009,"version":"3.40.3"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031726903"},{"type":"electronic","value":"9783031726910"}],"license":[{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:00:00Z","timestamp":1730592000000},"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\/978-3-031-72691-0_10","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T18:06:13Z","timestamp":1730570773000},"page":"166-182","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Leveraging Hierarchical Feature Sharing for\u00a0Efficient Dataset Condensation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0478-4028","authenticated-orcid":false,"given":"Haizhong","family":"Zheng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1170-4735","authenticated-orcid":false,"given":"Jiachen","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Shutong","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Bhavya","family":"Kailkhura","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9844-2055","authenticated-orcid":false,"given":"Z. Morley","family":"Mao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7043-4926","authenticated-orcid":false,"given":"Chaowei","family":"Xiao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4907-3687","authenticated-orcid":false,"given":"Atul","family":"Prakash","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Bang, J., Kim, H., Yoo, Y., Ha, J.W., Choi, J.: Rainbow memory: continual learning with a memory of diverse samples. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8218\u20138227 (2021)","DOI":"10.1109\/CVPR46437.2021.00812"},{"key":"10_CR2","first-page":"1","volume":"24","author":"BR Bartoldson","year":"2023","unstructured":"Bartoldson, B.R., Kailkhura, B., Blalock, D.: Compute-efficient deep learning: algorithmic trends and opportunities. J. Mach. Learn. Res. 24, 1\u201377 (2023)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR3","unstructured":"Brock, A., Lim, T., Ritchie, J.M., Weston, N.J.: Neural photo editing with introspective adversarial networks. In: 5th International Conference on Learning Representations 2017 (2017)"},{"key":"10_CR4","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, pp. 4750\u20134759 (2022)","DOI":"10.1109\/CVPR52688.2022.01045"},{"key":"10_CR5","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 Conference on Computer Vision and Pattern Recognition (CVPR) (2023)","DOI":"10.1109\/CVPR52729.2023.00364"},{"key":"10_CR6","unstructured":"Chai, L., Wulff, J., Isola, P.: Using latent space regression to analyze and leverage compositionality in GANs. In: International Conference on Learning Representations (2021)"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Chai, L., Zhu, J.Y., Shechtman, E., Isola, P., Zhang, R.: Ensembling with deep generative views. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14997\u201315007 (2021)","DOI":"10.1109\/CVPR46437.2021.01475"},{"key":"10_CR8","unstructured":"Chaudhry, A., et al.: On tiny episodic memories in continual learning. arXiv preprint arXiv:1902.10486 (2019)"},{"key":"10_CR9","unstructured":"Coleman, C., et al.: Selection via proxy: efficient data selection for deep learning. In: International Conference on Learning Representations (2019)"},{"key":"10_CR10","unstructured":"Cui, J., Wang, R., Si, S., Hsieh, C.J.: Dc-bench: dataset condensation benchmark. In: Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2022)"},{"key":"10_CR11","unstructured":"Cui, J., Wang, R., Si, S., Hsieh, C.J.: Scaling up dataset distillation to imagenet-1k with constant memory. In: International Conference on Machine Learning, pp. 6565\u20136590. PMLR (2023)"},{"key":"10_CR12","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":"10_CR13","unstructured":"Deng, Z., Russakovsky, O.: Remember the past: distilling datasets into addressable memories for neural networks. In: Advances in Neural Information Processing Systems (2022)"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Du, J., Jiang, Y., Tan, V.T., Zhou, J.T., Li, H.: Minimizing the accumulated trajectory error to improve dataset distillation. arXiv preprint arXiv:2211.11004 (2022)","DOI":"10.1109\/CVPR52729.2023.00365"},{"key":"10_CR15","unstructured":"Kim, J.H., et al.: Dataset condensation via efficient synthetic-data parameterization. In: International Conference on Machine Learning, pp. 11102\u201311118. PMLR (2022)"},{"key":"10_CR16","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Technical report Citeseer (2009)"},{"key":"10_CR17","unstructured":"Li, Y., Zhao, P., Lin, X., Kailkhura, B., Goldhahn, R.: Less is more: data pruning for faster adversarial training. arXiv preprint arXiv:2302.12366 (2023)"},{"key":"10_CR18","unstructured":"Liu, S., Wang, K., Yang, X., Ye, J., Wang, X.: Dataset distillation via factorization. In: Advances in Neural Information Processing Systems (2022)"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Gu, J., Wang, K., Zhu, Z., Jiang, W., You, Y.: Dream: efficient dataset distillation by representative matching. arXiv preprint arXiv:2302.14416 (2023)","DOI":"10.1109\/ICCV51070.2023.01588"},{"key":"10_CR20","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011 (2011). http:\/\/ufldl.stanford.edu\/housenumbers\/nips2011_housenumbers.pdf"},{"key":"10_CR21","unstructured":"Nguyen, T., Chen, Z., Lee, J.: Dataset meta-learning from kernel ridge-regression. In: International Conference on Learning Representations (2020)"},{"key":"10_CR22","unstructured":"Nguyen, T., Novak, R., Xiao, L., Lee, J.: Dataset distillation with infinitely wide convolutional networks. In: Advance in Neural Information Processing System, vol. 34, pp. 5186\u20135198 (2021)"},{"key":"10_CR23","unstructured":"Paul, M., Ganguli, S., Dziugaite, G.K.: Deep learning on a data diet: finding important examples early in training. In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"key":"10_CR24","unstructured":"Pleiss, G., Zhang, T., Elenberg, E., Weinberger, K.Q.: Identifying mislabeled data using the area under the margin ranking. In: Advances in Neural Information Processing Systems, vol. 33, pp. 17044\u201317056 (2020)"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: incremental classifier and representation learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2001\u20132010 (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"10_CR26","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1007\/978-3-031-17587-9_8","volume-title":"CSSL 2021","author":"A Rosasco","year":"2022","unstructured":"Rosasco, A., Carta, A., Cossu, A., Lomonaco, V., Bacciu, D.: Distilled replay: overcoming forgetting through synthetic samples. In: Cuzzolin, F., Cannons, K., Lomonaco, V. (eds.) CSSL 2021. LNCS, vol. 13418, pp. 104\u2013117. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-17587-9_8"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Sangermano, M., Carta, A., Cossu, A., Bacciu, D.: Sample condensation in online continual learning. In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 01\u201308. IEEE (2022)","DOI":"10.1109\/IJCNN55064.2022.9892299"},{"key":"10_CR28","unstructured":"Sener, O., Savarese, S.: Active learning for convolutional neural networks: a core-set approach. arXiv preprint arXiv:1708.00489 (2017)"},{"key":"10_CR29","unstructured":"Shin, S., Bae, H., Shin, D., Joo, W., Moon, I.C.: Loss-curvature matching for dataset selection and condensation. In: International Conference on Artificial Intelligence and Statistics, pp. 8606\u20138628. PMLR (2023)"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Song, R., et al.: Federated learning via decentralized dataset distillation in resource-constrained edge environments. arXiv preprint arXiv:2208.11311 (2022)","DOI":"10.1109\/IJCNN54540.2023.10191879"},{"key":"10_CR31","unstructured":"Sorscher, B., Geirhos, R., Shekhar, S., Ganguli, S., Morcos, A.S.: Beyond neural scaling laws: beating power law scaling via data pruning. arXiv preprint arXiv:2206.14486 (2022)"},{"key":"10_CR32","unstructured":"Toneva, M., Sordoni, A., des Combes, R.T., Trischler, A., Bengio, Y., Gordon, G.J.: An empirical study of example forgetting during deep neural network learning. In: International Conference on Learning Representations (2018)"},{"key":"10_CR33","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, pp. 12196\u201312205 (2022)","DOI":"10.1109\/CVPR52688.2022.01188"},{"key":"10_CR34","unstructured":"Wang, T., Zhu, J.Y., Torralba, A., Efros, A.A.: Dataset distillation. arXiv preprint arXiv:1811.10959 (2018)"},{"key":"10_CR35","unstructured":"Xia, X., Liu, J., Yu, J., Shen, X., Han, B., Liu, T.: Moderate coreset: a universal method of data selection for real-world data-efficient deep learning. In: International Conference on Learning Representations (2023)"},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Wang, R., Cheng, M., Yu, F., Hsieh, C.J.: Feddm: iterative distribution matching for communication-efficient federated learning. arXiv preprint arXiv:2207.09653 (2022)","DOI":"10.1109\/CVPR52729.2023.01566"},{"key":"10_CR37","unstructured":"Zhao, B., Bilen, H.: Dataset condensation with differentiable siamese augmentation. In: International Conference on Machine Learning, pp. 12674\u201312685. PMLR (2021)"},{"key":"10_CR38","unstructured":"Zhao, B., Bilen, H.: Synthesizing informative training samples with GAN. In: NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research (2022)"},{"key":"10_CR39","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, pp. 6514\u20136523 (2023)","DOI":"10.1109\/WACV56688.2023.00645"},{"key":"10_CR40","unstructured":"Zhao, B., Mopuri, K.R., Bilen, H.: Dataset condensation with gradient matching. In: Ninth International Conference on Learning Representations 2021 (2021)"},{"key":"10_CR41","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, pp. 7856\u20137865 (2023)","DOI":"10.1109\/CVPR52729.2023.00759"},{"key":"10_CR42","unstructured":"Zheng, H., Liu, R., Lai, F., Prakash, A.: Coverage-centric coreset selection for high pruning rates. In: International Conference on Learning Representations 2023 (2023)"},{"key":"10_CR43","unstructured":"Zheng, H., et al.: Elfs: enhancing label-free coreset selection via clustering-based pseudo-labeling. arXiv preprint arXiv:2406.04273 (2024)"},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Zheng, H., Zhang, Z., Gu, J., Lee, H., Prakash, A.: Efficient adversarial training with transferable adversarial examples. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1181\u20131190 (2020)","DOI":"10.1109\/CVPR42600.2020.00126"}],"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\/978-3-031-72691-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T18:07:08Z","timestamp":1730570828000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72691-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9783031726903","9783031726910"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72691-0_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,3]]},"assertion":[{"value":"3 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"}}]}}