{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T18:42:46Z","timestamp":1748716966877,"version":"3.40.3"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031729720"},{"type":"electronic","value":"9783031729737"}],"license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"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\/978-3-031-72973-7_13","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:03:04Z","timestamp":1730383384000},"page":"214-230","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["FYI: Flip Your Images for\u00a0Dataset Distillation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7344-0355","authenticated-orcid":false,"given":"Byunggwan","family":"Son","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5568-2127","authenticated-orcid":false,"given":"Youngmin","family":"Oh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2470-1469","authenticated-orcid":false,"given":"Donghyeon","family":"Baek","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3443-8161","authenticated-orcid":false,"given":"Bumsub","family":"Ham","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"13_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: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01045"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Cha, H., Lee, J., Shin, J.: Co$$^2$$L: Contrastive continual learning. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00938"},{"key":"13_CR3","unstructured":"Cui, J., Wang, R., Si, S., Hsieh, C.J.: Scaling up dataset distillation to imagenet-1k with constant memory. In: ICML (2023)"},{"key":"13_CR4","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: CVPR (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"13_CR5","unstructured":"Deng, Z., Russakovsky, O.: Remember the past: Distilling datasets into addressable memories for neural networks. In: NeurIPS (2022)"},{"key":"13_CR6","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"13_CR7","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: ICLR (2021)"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Du, J., Jiang, Y., Tan, V.T.F., Zhou, J.T., Li, H.: Minimizing the accumulated trajectory error to improve dataset distillation. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00365"},{"key":"13_CR9","unstructured":"Forgy, E.W.: Cluster analysis of multivariate data: efficiency versus interpretability of classifications. Biometrics (1965)"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Gidaris, S., Komodakis, N.: Dynamic few-shot visual learning without forgetting. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00459"},{"key":"13_CR11","unstructured":"Gretton, A., Borgwardt, K.M., Rasch, M.J., Sch\u00f6lkopf, B., Smola, A.: A kernel two-sample test. The Journal of Machine Learning Research (2012)"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Guo, Z., et al.: Single path one-shot neural architecture search with uniform sampling. In: ECCV (2020)","DOI":"10.1007\/978-3-030-58517-4_32"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"13_CR14","unstructured":"Howard, J.: A smaller subset of 10 easily classified classes from imagenet, and a little more french. https:\/\/github.com\/fastai\/imagenette (2019)"},{"key":"13_CR15","unstructured":"Jacot, A., Gabriel, F., Hongler, C.: Neural tangent kernel: Convergence and generalization in neural networks. In: NeurIPS (2018)"},{"key":"13_CR16","unstructured":"Kim, J.H., et al.: Dataset condensation via efficient synthetic-data parameterization. In: ICML (2022)"},{"key":"13_CR17","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al.: Learning multiple layers of features from tiny images. Technical report (2009)"},{"key":"13_CR18","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NeurIPS (2012)"},{"key":"13_CR19","unstructured":"Le, Y., Yang, X.: Tiny ImageNet visual recognition challenge. CS 231N (2015)"},{"key":"13_CR20","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE (1998)","DOI":"10.1109\/5.726791"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Lee, J., et al.: Wide neural networks of any depth evolve as linear models under gradient descent. In: NeurIPS (2019)","DOI":"10.1088\/1742-5468\/abc62b"},{"key":"13_CR22","unstructured":"Lee, S., Chun, S., Jung, S., Yun, S., Yoon, S.: Dataset condensation with contrastive signals. In: ICML (2022)"},{"key":"13_CR23","unstructured":"Li, T., Sahu, A.K., Zaheer, M., Sanjabi, M., Talwalkar, A., Smith, V.: Federated optimization in heterogeneous networks. In: MLSys (2020)"},{"key":"13_CR24","unstructured":"Li, X., Huang, K., Yang, W., Wang, S., Zhang, Z.: On the convergence of fedavg on non-iid data. In: ICLR (2020)"},{"key":"13_CR25","unstructured":"Liu, H., Simonyan, K., Yang, Y.: Darts: Differentiable architecture search. In: ICLR (2019)"},{"key":"13_CR26","unstructured":"Liu, S., Wang, K., Yang, X., Ye, J., Wang, X.: Dataset distillation via factorization. In: NeurIPS (2022)"},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Liu, Y., Gu, J., Wang, K., Zhu, Z., Jiang, W., You, Y.: DREAM: efficient dataset distillation by representative matching. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01588"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Mahajan, D., et al.: Exploring the limits of weakly supervised pretraining. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01216-8_12"},{"key":"13_CR29","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., y\u00a0Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: AISTATS (2017)"},{"key":"13_CR30","unstructured":"Nguyen, T., Chen, Z., Lee, J.: Dataset meta-learning from kernel ridge-regression. In: ICLR (2021)"},{"key":"13_CR31","unstructured":"Nguyen, T., Novak, R., Xiao, L., Lee, J.: Dataset distillation with infinitely wide convolutional networks. In: NeurIPS (2021)"},{"key":"13_CR32","unstructured":"Pham, H., Guan, M., Zoph, B., Le, Q., Dean, J.: Efficient neural architecture search via parameters sharing. In: ICML (2018)"},{"key":"13_CR33","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: NeurIPS (2021)"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: Incremental classifier and representation learning. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.587"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Sajedi, A., Khaki, S., Amjadian, E., Liu, L.Z., Lawryshyn, Y.A., Plataniotis, K.N.: DataDAM: efficient dataset distillation with attention matching. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01568"},{"key":"13_CR36","doi-asserted-by":"crossref","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)","DOI":"10.1109\/ICCV.2015.314"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Sun, C., Shrivastava, A., Singh, S., Gupta, A.: Revisiting unreasonable effectiveness of data in deep learning era. In: ECCV (2016)","DOI":"10.1109\/ICCV.2017.97"},{"key":"13_CR38","doi-asserted-by":"crossref","unstructured":"Wang, K., et al.: CAFE: Learning to condense dataset by aligning features. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.01188"},{"key":"13_CR39","unstructured":"Wang, T., Zhu, J.Y., Torralba, A., Efros, A.A.: Dataset distillation. arXiv preprint arXiv:1811.10959 (2018)"},{"key":"13_CR40","unstructured":"Wei, X., Cao, A., Yang, F., Ma, Z.: Sparse parameterization for epitomic dataset distillation. In: NeurIPS (2023)"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Yan, S., Xie, J., He, X.: DER: Dynamically expandable representation for class incremental learning. In: CVPR (2021)","DOI":"10.1109\/CVPR46437.2021.00303"},{"key":"13_CR42","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: CVPR (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, S., Wang, P., Zeng, D., Ge, S.: M3d: dataset condensation by minimizing maximum mean discrepancy. In: AAAI (2024)","DOI":"10.1609\/aaai.v38i8.28784"},{"key":"13_CR44","unstructured":"Zhao, B., Bilen, H.: Dataset condensation with differentiable siamese augmentation. In: ICML (2021)"},{"key":"13_CR45","unstructured":"Zhao, B., Bilen, H.: Dataset condensation with gradient matching. In: ICLR (2021)"},{"key":"13_CR46","doi-asserted-by":"crossref","unstructured":"Zhao, B., Bilen, H.: Dataset condensation with distribution matching. In: WACV (2023)","DOI":"10.1109\/WACV56688.2023.00645"},{"key":"13_CR47","doi-asserted-by":"crossref","unstructured":"Zhao, G., Li, G., Qin, Y., Yu, Y.: Improved distribution matching for dataset condensation. In: CVPR (2023)","DOI":"10.1109\/CVPR52729.2023.00759"},{"key":"13_CR48","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.319"},{"key":"13_CR49","unstructured":"Zhou, Y., Nezhadarya, E., Ba, J.: Dataset distillation using neural feature regression. In: NeurIPS (2022)"},{"key":"13_CR50","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V.: Learning transferable architectures for scalable image recognition. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00907"}],"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\/978-3-031-72973-7_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T14:10:59Z","timestamp":1730383859000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72973-7_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,1]]},"ISBN":["9783031729720","9783031729737"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72973-7_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,1]]},"assertion":[{"value":"1 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"}}]}}