{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T05:05:35Z","timestamp":1765343135496,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","funder":[{"name":"Beijing Natural Science Foundation","award":["JQ24022, No. L251005"],"award-info":[{"award-number":["JQ24022, No. L251005"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62192785, No. 62372451, No. 62372082, No. 62272125, No. 62306312, No. 62036011, No. 62192782, No. U24A20331"],"award-info":[{"award-number":["No. 62192785, No. 62372451, No. 62372082, No. 62272125, No. 62306312, No. 62036011, No. 62192782, No. U24A20331"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CAAI-Ant Group Research Fund","award":["CAAI-MYJJ 2024-02"],"award-info":[{"award-number":["CAAI-MYJJ 2024-02"]}]},{"name":"Young Elite Scientists Sponsorship Program by CAST","award":["2024QNRC001"],"award-info":[{"award-number":["2024QNRC001"]}]},{"name":"the Project of Beijing Science and Technology Committee","award":["No. Z231100005923046"],"award-info":[{"award-number":["No. Z231100005923046"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,27]]},"DOI":"10.1145\/3746027.3755602","type":"proceedings-article","created":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T07:27:39Z","timestamp":1761377259000},"page":"10352-10360","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Noise-Optimized Distribution Distillation for Dataset Condensation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0517-0896","authenticated-orcid":false,"given":"Tongfei","family":"Liu","sequence":"first","affiliation":[{"name":"MAIS, CASIA, Beijing, China and School of Artificial Intelligence, UCAS, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8426-9335","authenticated-orcid":false,"given":"Yufan","family":"Liu","sequence":"additional","affiliation":[{"name":"MAIS, CASIA, Beijing, China and School of Artificial Intelligence, UCAS, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6114-1411","authenticated-orcid":false,"given":"Bing","family":"Li","sequence":"additional","affiliation":[{"name":"MAIS, CASIA, Beijing, China and PeopleAI, Inc., Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9237-8825","authenticated-orcid":false,"given":"Weiming","family":"Hu","sequence":"additional","affiliation":[{"name":"MAIS, CASIA, Beijing, China, School of Artificial Intelligence, UCAS, Beijing, China, and SIST, ShanghaiTech, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4781-9707","authenticated-orcid":false,"given":"Yuming","family":"Li","sequence":"additional","affiliation":[{"name":"Terminal Technology Department, Alipay, Ant Group, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3627-2740","authenticated-orcid":false,"given":"Chenguang","family":"Ma","sequence":"additional","affiliation":[{"name":"Terminal Technology Department, Alipay, Ant Group, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3361474"},{"key":"e_1_3_2_1_2_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4750-4759","author":"Cazenavette George","year":"2022","unstructured":"George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. 2022. Dataset distillation by matching training trajectories. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4750-4759."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00364"},{"key":"e_1_3_2_1_4_1","volume-title":"The Thirteenth International Conference on Learning Representations.","author":"Chen Mingyang","year":"2025","unstructured":"Mingyang Chen, Jiawei Du, Bo Huang, Yi Wang, Xiaobo Zhang, and Wei Wang. 2025. Influence-guided diffusion for dataset distillation. In The Thirteenth International Conference on Learning Representations."},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Machine Learning. PMLR, 6565-6590","author":"Cui Justin","year":"2023","unstructured":"Justin Cui, Ruochen Wang, Si Si, and Cho-Jui Hsieh. 2023. Scaling up dataset distillation to imagenet-1k with constant memory. In International Conference on Machine Learning. PMLR, 6565-6590."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_7_1","first-page":"34391","article-title":"Remember the past: Distilling datasets into addressable memories for neural networks","volume":"35","author":"Deng Zhiwei","year":"2022","unstructured":"Zhiwei Deng and Olga Russakovsky. 2022. Remember the past: Distilling datasets into addressable memories for neural networks. Advances in Neural Information Processing Systems, Vol. 35, 34391-34404.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00365"},{"key":"e_1_3_2_1_9_1","volume-title":"Embarrassingly Simple Dataset Distillation. In Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ NeurIPS","author":"Feng Yunzhen","year":"2023","unstructured":"Yunzhen Feng, Shanmukha Ramakrishna Vedantam, and Julia Kempe. [n.d.]. Embarrassingly Simple Dataset Distillation. In Workshop on Advancing Neural Network Training: Computational Efficiency, Scalability, and Resource Optimization (WANT@ NeurIPS 2023)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2188410"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01495"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_13_1","volume-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems","author":"Heusel Martin","year":"2017","unstructured":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. 2017. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_14_1","volume-title":"Denoising diffusion probabilistic models. Advances in neural information processing systems","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems, Vol. 33 (2020), 6840-6851."},{"key":"e_1_3_2_1_15_1","volume-title":"Imagenette: A smaller subset of 10 easily classified classes from imagenet. https:\/\/github.com\/fastai\/imagenette","author":"Howard Jeremy","year":"2019","unstructured":"Jeremy Howard. 2019. Imagenette: A smaller subset of 10 easily classified classes from imagenet. https:\/\/github.com\/fastai\/imagenette"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigCom53800.2021.00042"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1587\/transfun.2023EAL2053"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00762"},{"key":"e_1_3_2_1_19_1","volume-title":"European Conference on Computer Vision. Springer, 334-351","author":"Liu Dai","year":"2024","unstructured":"Dai Liu, Jindong Gu, Hu Cao, Carsten Trinitis, and Martin Schulz. 2024. Dataset distillation by automatic training trajectories. In European Conference on Computer Vision. Springer, 334-351."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01588"},{"key":"e_1_3_2_1_21_1","first-page":"13877","article-title":"Efficient dataset distillation using random feature approximation","volume":"35","author":"Loo Noel","year":"2022","unstructured":"Noel Loo, Ramin Hasani, Alexander Amini, and Daniela Rus. 2022. Efficient dataset distillation using random feature approximation. Advances in Neural Information Processing Systems, Vol. 35, 13877-13891.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_22_1","volume-title":"Latent dataset distillation with diffusion models. arXiv preprint arXiv:2403.03881","author":"Moser Brian B","year":"2024","unstructured":"Brian B Moser, Federico Raue, Sebastian Palacio, Stanislav Frolov, and Andreas Dengel. 2024. Latent dataset distillation with diffusion models. arXiv preprint arXiv:2403.03881 (2024)."},{"volume-title":"International Conference on Learning Representations.","author":"Nguyen Timothy","key":"e_1_3_2_1_23_1","unstructured":"Timothy Nguyen, Zhourong Chen, and Jaehoon Lee. [n.d.]. Dataset Meta-Learning from Kernel Ridge-Regression. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_24_1","volume-title":"Scalable Diffusion Models with Transformers. arXiv preprint arXiv:2212.09748","author":"Peebles William","year":"2022","unstructured":"William Peebles and Saining Xie. 2022. Scalable Diffusion Models with Transformers. arXiv preprint arXiv:2212.09748 (2022)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Robin Rombach Andreas Blattmann Dominik Lorenz Patrick Esser and Bj\u00f6rn Ommer. 2021. High-Resolution Image Synthesis with Latent Diffusion Models. arXiv:2112.10752 [cs.CV]","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3528233.3530738"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01581"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00897"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_3_2_1_30_1","volume-title":"Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022","author":"Ulyanov Dmitry","year":"2016","unstructured":"Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. 2016. Instance normalization: The missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)."},{"key":"e_1_3_2_1_31_1","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research, Vol. 9, 86 (2008), 2579-2605. http:\/\/jmlr.org\/papers\/v9\/vandermaaten08a.html","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_32_1","volume-title":"Dim: Distilling dataset into generative model. arXiv preprint arXiv:2303.04707","author":"Wang Kai","year":"2023","unstructured":"Kai Wang, Jianyang Gu, Daquan Zhou, Zheng Zhu, Wei Jiang, and Yang You. 2023. Dim: Distilling dataset into generative model. arXiv preprint arXiv:2303.04707 (2023)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01188"},{"key":"e_1_3_2_1_34_1","volume-title":"Dataset distillation. arXiv preprint arXiv:1811.10959","author":"Wang Tongzhou","year":"2018","unstructured":"Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. 2018. Dataset distillation. arXiv preprint arXiv:1811.10959 (2018)."},{"key":"e_1_3_2_1_35_1","first-page":"73582","article-title":"Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective","volume":"36","author":"Yin Zeyuan","year":"2023","unstructured":"Zeyuan Yin, Eric Xing, and Zhiqiang Shen. 2023. Squeeze, recover and relabel: Dataset condensation at imagenet scale from a new perspective. Advances in Neural Information Processing Systems, Vol. 36, 73582-73603.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_36_1","volume-title":"European Conference on Computer Vision. Springer, 1-17","author":"Yu Ruonan","year":"2024","unstructured":"Ruonan Yu, Songhua Liu, Jingwen Ye, and Xinchao Wang. 2024. Teddy: Efficient large-scale dataset distillation via taylor-approximated matching. In European Conference on Computer Vision. Springer, 1-17."},{"key":"e_1_3_2_1_37_1","volume-title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. 1679-1687","author":"Zhang Hansong","year":"2024","unstructured":"Hansong Zhang, Shikun Li, Fanzhao Lin, Weiping Wang, Zhenxing Qian, and Shiming Ge. 2024. DANCE: dual-view distribution alignment for dataset condensation. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. 1679-1687."},{"key":"e_1_3_2_1_38_1","volume-title":"International Conference on Machine Learning. PMLR, 12674-12685","author":"Zhao Bo","year":"2021","unstructured":"Bo Zhao and Hakan Bilen. 2021. Dataset condensation with differentiable siamese augmentation. In International Conference on Machine Learning. PMLR, 12674-12685."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00645"},{"key":"e_1_3_2_1_40_1","volume-title":"Synthesizing Informative Training Samples with GAN. In NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research.","author":"Zhao Bo","year":"2023","unstructured":"Bo Zhao and Hakan Bilen. 2023b. Synthesizing Informative Training Samples with GAN. In NeurIPS 2022 Workshop on Synthetic Data for Empowering ML Research."},{"key":"e_1_3_2_1_41_1","volume-title":"Dataset Condensation with Gradient Matching. In Ninth International Conference on Learning Representations","author":"Zhao Bo","year":"2021","unstructured":"Bo Zhao, Konda Reddy Mopuri, and Hakan Bilen. 2021. Dataset Condensation with Gradient Matching. In Ninth International Conference on Learning Representations 2021."},{"key":"e_1_3_2_1_42_1","first-page":"9813","article-title":"Dataset distillation using neural feature regression","volume":"35","author":"Zhou Yongchao","year":"2022","unstructured":"Yongchao Zhou, Ehsan Nezhadarya, and Jimmy Ba. 2022. Dataset distillation using neural feature regression. Advances in Neural Information Processing Systems, Vol. 35, 9813-9827.","journal-title":"Advances in Neural Information Processing Systems"}],"event":{"name":"MM '25: The 33rd ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Dublin Ireland","acronym":"MM '25"},"container-title":["Proceedings of the 33rd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746027.3755602","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T05:02:03Z","timestamp":1765342923000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746027.3755602"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":42,"alternative-id":["10.1145\/3746027.3755602","10.1145\/3746027"],"URL":"https:\/\/doi.org\/10.1145\/3746027.3755602","relation":{},"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}