{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T19:12:26Z","timestamp":1776107546824,"version":"3.50.1"},"publisher-location":"Cham","reference-count":58,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031124228","type":"print"},{"value":"9783031124235","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-12423-5_14","type":"book-chapter","created":{"date-parts":[[2022,7,28]],"date-time":"2022-07-28T17:03:28Z","timestamp":1659027808000},"page":"181-195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["DeepCore: A Comprehensive Library for\u00a0Coreset Selection in\u00a0Deep Learning"],"prefix":"10.1007","author":[{"given":"Chengcheng","family":"Guo","sequence":"first","affiliation":[]},{"given":"Bo","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yanbing","family":"Bai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,29]]},"reference":[{"key":"14_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-58517-4_9","volume-title":"Computer Vision \u2013 ECCV 2020","author":"S Agarwal","year":"2020","unstructured":"Agarwal, S., Arora, H., Anand, S., Arora, C.: Contextual diversity for active learning. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 137\u2013153. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58517-4_9"},{"key":"14_CR2","first-page":"11816","volume":"32","author":"R Aljundi","year":"2019","unstructured":"Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Gradient based sample selection for online continual learning. Adv. Neural. Inf. Process. Syst. 32, 11816\u201311825 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"14_CR3","unstructured":"Bachem, O., Lucic, M., Krause, A.: Coresets for nonparametric estimation-the case of dp-means. In: ICML, PMLR, pp. 209\u2013217 (2015)"},{"key":"14_CR4","unstructured":"Bateni, M., Bhaskara, A., Lattanzi, S., Mirrokni, V.S.: Distributed balanced clustering via mapping coresets. In: NIPS, pp. 2591\u20132599 (2014)"},{"key":"14_CR5","unstructured":"Borsos, Z., Mutny, M., Krause, A.: Coresets via bilevel optimization for continual learning and streaming. In: Advances in Neural Information Processing Systems, vol. 33 (2020)"},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Borsos, Z., Tagliasacchi, M., Krause, A.: Semi-supervised batch active learning via bilevel optimization. In: ICASSP 2021, pp. 3495\u20133499. IEEE (2021)","DOI":"10.1109\/ICASSP39728.2021.9414206"},{"key":"14_CR7","unstructured":"Chen, Y., Welling, M., Smola, A.: Super-samples from kernel herding. In: The Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (2010)"},{"key":"14_CR8","unstructured":"Chhaya, R., Dasgupta, A., Shit, S.: On coresets for regularized regression. In: International Conference on Machine Learning, PMLR, pp. 1866\u20131876 (2020)"},{"key":"14_CR9","unstructured":"Coleman, C., et al.: Selection via proxy: efficient data selection for deep learning. In: ICLR (2019)"},{"key":"14_CR10","unstructured":"Dasgupta, S., Hsu, D., Poulis, S., Zhu, X.: Teaching a black-box learner. In: ICML, PMLR (2019)"},{"key":"14_CR11","unstructured":"Ducoffe, M., Precioso, F.: Adversarial active learning for deep networks: a margin based approach (2018). arXiv preprint arXiv:1802.09841"},{"key":"14_CR12","unstructured":"Farahani, R.Z., Hekmatfar, M.: Facility location: concepts, models, algorithms and case studies (2009)"},{"key":"14_CR13","unstructured":"Feldman, D., Faulkner, M., Krause, A.: Scalable training of mixture models via coresets. In: NIPS, Citeseer, pp. 2142\u20132150 (2011)"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Howard, A., et al.: Searching for mobilenetv3 (2019). http:\/\/arxiv.org\/abs\/1905.02244","DOI":"10.1109\/ICCV.2019.00140"},{"key":"14_CR16","unstructured":"Iyer, R., Khargoankar, N., Bilmes, J., Asanani, H.: Submodular combinatorial information measures with applications in machine learning. In: Algorithmic Learning Theory, pp. 722\u2013754. PMLR (2021)"},{"key":"14_CR17","unstructured":"Iyer, R.K., Bilmes, J.A.: Submodular optimization with submodular cover and submodular knapsack constraints. In: Advances in Neural Information Processing Systems, vol. 26 (2013)"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Ju, J., Jung, H., Oh, Y., Kim, J.: Extending contrastive learning to unsupervised coreset selection (2021). arXiv preprint arXiv:2103.03574","DOI":"10.1109\/ACCESS.2022.3142758"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Kaushal, V., Kothawade, S., Ramakrishnan, G., Bilmes, J., Iyer, R.: Prism: A unified framework of parameterized submodular information measures for targeted data subset selection and summarization (2021). arXiv preprint arXiv:2103.00128","DOI":"10.1609\/aaai.v36i9.21264"},{"key":"14_CR20","unstructured":"Killamsetty, K., Durga, S., Ramakrishnan, G., De, A., Iyer, R.: Grad-match: gradient matching based data subset selection for efficient deep model training. In: ICML, pp. 5464\u20135474 (2021)"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Killamsetty, K., Sivasubramanian, D., Ramakrishnan, G., Iyer, R.: Glister: generalization based data subset selection for efficient and robust learning. In: Proceedings of the AAAI Conference on Artificial Intelligence (2021)","DOI":"10.1609\/aaai.v35i9.16988"},{"key":"14_CR22","unstructured":"Killamsetty, K., Zhao, X., Chen, F., Iyer, R.: Retrieve: Coreset selection for efficient and robust semi-supervised learning (2021). arXiv preprint arXiv:2106.07760"},{"key":"14_CR23","unstructured":"Knoblauch, J., Husain, H., Diethe, T.: Optimal continual learning has perfect memory and is np-hard. In: International Conference on Machine Learning, PMLR, pp. 5327\u20135337 (2020)"},{"key":"14_CR24","unstructured":"Kothawade, S., Beck, N., Killamsetty, K., Iyer, R.: Similar: Submodular information measures based active learning in realistic scenarios (2021). arXiv preprint arXiv:2107.00717"},{"key":"14_CR25","unstructured":"Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)"},{"key":"14_CR26","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C.J.C., Bottou, L., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 25. Curran Associates, Inc. (2012)"},{"key":"14_CR27","unstructured":"Le, Y., Yang, X.: Tiny imagenet visual recognition challenge. CS 231N 7(7), 3 (2015)"},{"issue":"4","key":"14_CR28","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., Jackel, L.D.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989)","journal-title":"Neural Comput."},{"issue":"11","key":"14_CR29","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"14_CR30","unstructured":"Liu, E.Z., et al.: Just train twice: Improving group robustness without training group information. In: ICML, pp. 6781\u20136792 (2021)"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Margatina, K., Vernikos, G., Barrault, L., Aletras, N.: Active learning by acquiring contrastive examples (2021). arXiv preprint arXiv:2109.03764","DOI":"10.18653\/v1\/2021.emnlp-main.51"},{"key":"14_CR32","unstructured":"Mirzasoleiman, B., Bilmes, J., Leskovec, J.: Coresets for data-efficient training of machine learning models. In: ICML, PMLR (2020)"},{"key":"14_CR33","unstructured":"Mirzasoleiman, B., Cao, K., Leskovec, J.: Coresets for robust training of deep neural networks against noisy labels (2020)"},{"key":"14_CR34","unstructured":"Munteanu, A., Schwiegelshohn, C., Sohler, C., Woodruff, D.P.: On coresets for logistic regression. In: NeurIPS (2018)"},{"issue":"1","key":"14_CR35","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1007\/BF01588971","volume":"14","author":"GL Nemhauser","year":"1978","unstructured":"Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functions-i. Math. Program. 14(1), 265\u2013294 (1978)","journal-title":"Math. Program."},{"key":"14_CR36","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning (2011)"},{"key":"14_CR37","unstructured":"Paszke, A., et al.: Pytorch: An imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"14_CR38","unstructured":"Paul, M., Ganguli, S., Dziugaite, G.K.: Deep learning on a data diet: finding important examples early in training (2021). arXiv preprint arXiv:2107.07075"},{"key":"14_CR39","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., et al.: ImageNet Large Scale Visual Recognition Challenge. In: IJCV (2015)","DOI":"10.1007\/s11263-015-0816-y"},{"key":"14_CR40","unstructured":"Sachdeva, N., Wu, C.J., McAuley, J.: Svp-cf: selection via proxy for collaborative filtering data (2021). arXiv preprint arXiv:2107.04984 (2021)"},{"key":"14_CR41","unstructured":"Sener, O., Savarese, S.: Active learning for convolutional neural networks: a core-set approach. In: ICLR (2018)"},{"key":"14_CR42","unstructured":"Settles, B.: Active learning literature survey (2009)"},{"key":"14_CR43","unstructured":"Settles, B.: From theories to queries: Active learning in practice. In: Active Learning and Experimental Design Workshop in Conjunction with AISTATS 2010, JMLR Workshop and Conference Proceedings, pp. 1\u201318 (2011)"},{"key":"14_CR44","unstructured":"Shim, J.h., Kong, K., Kang, S.J.: Core-set sampling for efficient neural architecture search (2021). arXiv preprint arXiv:2107.06869"},{"key":"14_CR45","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556"},{"key":"14_CR46","unstructured":"Sinha, S., Zhang, H., Goyal, A., Bengio, Y., Larochelle, H., Odena, A.: Small-gan: Speeding up gan training using core-sets. In: ICML, PMLR (2020)"},{"key":"14_CR47","doi-asserted-by":"crossref","unstructured":"Sohler, C., Woodruff, D.P.: Strong coresets for k-median and subspace approximation: goodbye dimension. In: 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS), pp. 802\u2013813. IEEE (2018)","DOI":"10.1109\/FOCS.2018.00081"},{"key":"14_CR48","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"14_CR49","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 (2018)"},{"key":"14_CR50","unstructured":"Wang, T., Zhu, J.Y., Torralba, A., Efros, A.A.: Dataset distillation (2018). arXiv preprint arXiv:1811.10959"},{"key":"14_CR51","unstructured":"Wei, K., Iyer, R., Bilmes, J.: Submodularity in data subset selection and active learning. In: International Conference on Machine Learning, PMLR (2015)"},{"key":"14_CR52","doi-asserted-by":"crossref","unstructured":"Welling, M.: Herding dynamical weights to learn. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 1121\u20131128 (2009)","DOI":"10.1145\/1553374.1553517"},{"key":"14_CR53","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":"14_CR54","unstructured":"Yadav, C., Bottou, L.: Cold case: The lost mnist digits. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"14_CR55","unstructured":"Yoon, J., Madaan, D., Yang, E., Hwang, S.J.: Online coreset selection for rehearsal-based continual learning (2021). arXiv preprint arXiv:2106.01085"},{"key":"14_CR56","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks (2016). arXiv preprint arXiv:1605.07146","DOI":"10.5244\/C.30.87"},{"key":"14_CR57","unstructured":"Zhao, B., Bilen, H.: Dataset condensation with differentiable siamese augmentation. In: International Conference on Machine Learning (2021)"},{"key":"14_CR58","unstructured":"Zhao, B., Mopuri, K.R., Bilen, H.: Dataset condensation with gradient matching. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=mSAKhLYLSsl"}],"container-title":["Lecture Notes in Computer Science","Database and Expert Systems Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-12423-5_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:59:27Z","timestamp":1710359967000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-12423-5_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031124228","9783031124235"],"references-count":58,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-12423-5_14","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"29 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DEXA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database and Expert Systems Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vienna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 August 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"33","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dexa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.dexa.org\/dexa2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"120","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Mixed review process- Single and double blind","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}