{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T16:49:14Z","timestamp":1783356554107,"version":"3.54.6"},"reference-count":80,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202170"],"award-info":[{"award-number":["62202170"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62236008"],"award-info":[{"award-number":["62236008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/tmm.2025.3618578","type":"journal-article","created":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T17:46:21Z","timestamp":1759859181000},"page":"9873-9886","source":"Crossref","is-referenced-by-count":1,"title":["Boosting Dataset Distillation With the Assistance of Crucial Samples for Visual Learning"],"prefix":"10.1109","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5025-4805","authenticated-orcid":false,"given":"Xiaodan","family":"Li","sequence":"first","affiliation":[{"name":"School of Data Science and Engineering, East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0991-1970","authenticated-orcid":false,"given":"Yao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Qiyuan Laboratory, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9027-3421","authenticated-orcid":false,"given":"Yuefeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0325-1705","authenticated-orcid":false,"given":"Cen","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Data Science and Engineering, East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5787-6781","authenticated-orcid":false,"given":"Jianmei","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Data Science and Engineering, East China Normal University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5931-0527","authenticated-orcid":false,"given":"Shuhui","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-024-4231-5"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2023.3266172"},{"key":"ref5","first-page":"68895","article-title":"Label-only model inversion attacks via knowledge transfer","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"36","author":"Nguyen","year":"2023"},{"key":"ref6","article-title":"GPT-4 technical report","author":"Achiam","year":"2023"},{"key":"ref7","article-title":"LlAMA 2: Open foundation and fine-tuned chat models","author":"Touvron","year":"2023"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3262180"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3263552"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s42003-023-04488-9"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1126\/science.ade2574"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3321501"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3323878"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3335875"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3310277"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3291588"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3322540"},{"key":"ref18","first-page":"9813","article-title":"Dataset distillation using neural feature regression","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Zhou","year":"2022"},{"key":"ref19","first-page":"22 649","article-title":"Dataset distillation with convexified implicit gradients","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Loo","year":"2023"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3057446"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3305871"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-05318-5_3"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3304892"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s10207-021-00564-5"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3257566"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00645"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00759"},{"key":"ref28","article-title":"Dataset condensation with gradient matching","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Zhao","year":"2021"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01045"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00365"},{"key":"ref31","first-page":"13 877","article-title":"Efficient dataset distillation using random feature approximation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"35","author":"Loo","year":"2022"},{"key":"ref32","first-page":"5186","article-title":"Dataset distillation with infinitely wide convolutional networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Nguyen","year":"2021"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN54540.2023.10191879"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2865686"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3234822"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3109419"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2919431"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3243616"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3323376"},{"key":"ref40","article-title":"Video generation models as world simulators","author":"Brooks","year":"2024"},{"key":"ref41","first-page":"8748","article-title":"Learning transferable visual models from natural language supervision","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Radford","year":"2021"},{"key":"ref42","article-title":"LLaVA-next: Improved reasoning, OCR, and world knowledge","author":"Liu","year":"2024"},{"key":"ref43","first-page":"25278","article-title":"LAION-5B: An open large-scale dataset for training next generation image-text models","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Schuhmann","year":"2022"},{"key":"ref44","article-title":"Amazon mechanical turk","volume":"17","author":"Turk","year":"2012","journal-title":"Retrieved"},{"key":"ref45","first-page":"17848","article-title":"Adaptive second order coresets for data-efficient machine learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Pooladzandi","year":"2022"},{"key":"ref46","first-page":"39314","article-title":"Towards sustainable learning: Coresets for data-efficient deep learning","volume-title":"Proc. Proc. 40th Int. Conf. Mach. Learn.","author":"Yang","year":"2023"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP49357.2023.10095172"},{"key":"ref48","first-page":"8606","article-title":"Loss-curvature matching for dataset selection and condensation","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Shin","year":"2023"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01188"},{"key":"ref50","first-page":"11102","article-title":"Dataset condensation via efficient synthetic-data parameterization","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Kim","year":"2022"},{"key":"ref51","article-title":"Synthesizing informative training samples with GAN","volume-title":"Proc. NeurIPS 2022 Workshop Synthetic Data Empower. ML Res.","author":"Zhao","year":"2023"},{"key":"ref52","first-page":"56437","article-title":"MGDD: A meta generator for fast dataset distillation","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Liu","year":"2024"},{"key":"ref53","article-title":"Improved regularization of convolutional neural networks with cutout","author":"DeVries","year":"2017"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00612"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-30164-8_251"},{"key":"ref56","first-page":"6438","article-title":"Manifold mixup: Better representations by interpolating hidden states","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Verma","year":"2019"},{"key":"ref57","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.89"},{"key":"ref59","first-page":"5234","article-title":"Assessing generative models via precision and recall","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Sajjadi","year":"2018"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01858"},{"key":"ref61","first-page":"12674","article-title":"Dataset condensation with differentiable siamese augmentation","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhao","year":"2021"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref63","article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"issue":"7","key":"ref64","article-title":"Tiny imagenet visual recognition challenge","volume":"7","author":"Le","year":"2015","journal-title":"CS 231 N"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref66","article-title":"A smaller subset of 10 easily classified classes from imageNet, and a little more french","author":"Howard","year":"2019"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00365"},{"key":"ref68","first-page":"6565","article-title":"Scaling up dataset distillation to imageNet-1 k with constant memory","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cui","year":"2023"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref70","article-title":"Very deep convolutional networks for large-scale image recognition","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Simonyan","year":"2015"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.5555\/3045118.3045167"},{"key":"ref73","article-title":"Instance normalization: The missing ingredient for fast stylization","author":"Ulyanov","year":"2016"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00257-z"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01588"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_31"},{"key":"ref77","first-page":"109","article-title":"Super-samples from kernel herding","volume-title":"Proc. 26th Conf. Uncertain. Artif. Intell.","author":"Chen","year":"2010"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1145\/3523273"},{"key":"ref79","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref80","article-title":"Tensorflow\/privacy: Library for training machine learning models with privacy for training data","year":"2019"}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6046\/10844992\/11195723.pdf?arnumber=11195723","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T18:34:27Z","timestamp":1766082867000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11195723\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":80,"URL":"https:\/\/doi.org\/10.1109\/tmm.2025.3618578","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}