{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:14:34Z","timestamp":1743005674785,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819787944"},{"type":"electronic","value":"9789819787951"}],"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\/978-981-97-8795-1_3","type":"book-chapter","created":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T23:01:57Z","timestamp":1730588517000},"page":"36-50","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["UDD: Dataset Distillation via Mining Underutilized Regions"],"prefix":"10.1007","author":[{"given":"Shiguang","family":"Wang","sequence":"first","affiliation":[]},{"given":"Zhongyu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jian","family":"Cheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,3]]},"reference":[{"key":"3_CR1","unstructured":"Bohdal, O., Yang, Y., Hospedales, T.M.: Flexible dataset distillation: learn labels instead of images (2020). arXiv preprint arXiv:2006.08572"},{"key":"3_CR2","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s41095-019-0149-9","volume":"5","author":"A Borji","year":"2019","unstructured":"Borji, A., Cheng, M.M., Hou, Q., Jiang, H., Li, J.: Salient object detection: a survey. Comput. Vis. Med. 5, 117\u2013150 (2019)","journal-title":"Comput. Vis. Med."},{"issue":"12","key":"3_CR3","doi-asserted-by":"publisher","first-page":"5706","DOI":"10.1109\/TIP.2015.2487833","volume":"24","author":"A Borji","year":"2015","unstructured":"Borji, A., Cheng, M.M., Jiang, H., Li, J.: Salient object detection: a benchmark. IEEE Trans. Image Process. 24(12), 5706\u20135722 (2015)","journal-title":"IEEE Trans. Image Process."},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Cazenavette, G., Wang, T., Torralba, A., Efros, A.A., Zhu, J.: Dataset distillation by matching training trajectories. In: CVPR, pp. 10708\u201310717 (2022)","DOI":"10.1109\/CVPR52688.2022.01045"},{"key":"3_CR5","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.E.: A simple framework for contrastive learning of visual representations. In: ICML, pp. 1597\u20131607 (2020)"},{"key":"3_CR6","unstructured":"Chen, Y., Welling, M., Smola, A.: Super-samples from kernel herding. In: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence, pp. 109\u2013116 (2010)"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Deng, L.: The mnist database of handwritten digit images for machine learning research. IEEE Sig. Process. Mag. 29(6), 141\u2013142 (2012)","DOI":"10.1109\/MSP.2012.2211477"},{"key":"3_CR8","unstructured":"Goetz, J., Tewari, A.: Federated learning via synthetic data (2020). arXiv preprint arXiv:2008.04489"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Gonz\u00e1lez, R.C., Woods, R.E.: Digital image processing. PAMI, pp. 242\u2013243 (1981)","DOI":"10.1109\/TPAMI.1981.4767088"},{"key":"3_CR10","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. Stat 1050, 9 (2015)"},{"key":"3_CR11","unstructured":"Kim, J., Kim, J., Oh, S.J., Yun, S., Song, H., Jeong, J., Ha, J., Song, H.O.: Dataset condensation via efficient synthetic-data parameterization. In: ICML, pp. 11102\u201311118 (2022)"},{"key":"3_CR12","unstructured":"Krizhevsky, A., Hinton, G., et\u00a0al: Learning multiple layers of features from tiny images (2009)"},{"key":"3_CR13","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp. 1106\u20131114 (2012)"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE, 2278\u20132324 (1998)","DOI":"10.1109\/5.726791"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Li, G., Togo, R., Ogawa, T., Haseyama, M.: Soft-label anonymous gastric x-ray image distillation. In: ICIP, pp. 305\u2013309 (2020)","DOI":"10.1109\/ICIP40778.2020.9191357"},{"key":"3_CR16","unstructured":"Van\u00a0der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. (2008)"},{"key":"3_CR17","unstructured":"Maclaurin, D., Duvenaud, D., Adams, R.P.: Gradient-based hyperparameter optimization through reversible learning. In: ICML, pp. 2113\u20132122 (2015)"},{"key":"3_CR18","unstructured":"Nguyen, T., Chen, Z., Lee, J.: Dataset meta-learning from kernel ridge-regression. In: ICLR (2021)"},{"key":"3_CR19","unstructured":"Nguyen, T., Novak, R., Xiao, L., Lee, J.: Dataset distillation with infinitely wide convolutional networks. In: NeurIPS, pp. 5186\u20135198 (2021)"},{"key":"3_CR20","unstructured":"Sermanet, P., Chintala, S., LeCun, Y.: Convolutional neural networks applied to house numbers digit classification. In: ICPR, pp. 3288\u20133291 (2012)"},{"key":"3_CR21","unstructured":"Such, F.P., Rawal, A., Lehman, J., Stanley, K.O., Clune, J.: Generative teaching networks: accelerating neural architecture search by learning to generate synthetic training data. In: ICML, pp. 9206\u20139216 (2020)"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Sucholutsky, I., Schonlau, M.: Secdd: Efficient and secure method for remotely training neural networks (student abstract). In: AAAI, pp. 15897\u201315898 (2021)","DOI":"10.1609\/aaai.v35i18.17945"},{"key":"3_CR23","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: ICLR (2019)"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Wang, K., Zhao, B., Peng, X., Zhu, Z., Yang, S., Wang, S., Huang, G., Bilen, H., Wang, X., You, Y.: CAFE: learning to condense dataset by aligning features. In: CVPR, pp. 12186\u201312195 (2022)","DOI":"10.1109\/CVPR52688.2022.01188"},{"key":"3_CR25","unstructured":"Wang, T., Zhu, J., Torralba, A., Efros, A.A.: Dataset distillation. arXiv preprint arXiv:1811.10959 (2018)"},{"key":"3_CR26","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms (2017). arXiv preprint arXiv:1708.07747"},{"key":"3_CR27","unstructured":"Zhao, B., Mopuri, K.R., Bilen, H.: Dataset condensation with gradient matching. In: ICLR (2021)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8795-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T23:02:03Z","timestamp":1730588523000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8795-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,3]]},"ISBN":["9789819787944","9789819787951"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8795-1_3","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":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}