{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T13:19:55Z","timestamp":1788527995017,"version":"build-2803163510"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T00:00:00Z","timestamp":1783209600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.patcog.2026.114374","type":"journal-article","created":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T15:08:41Z","timestamp":1783177721000},"page":"114374","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PD","title":["DBCore: Shaping generalizable decision boundaries for coreset selection"],"prefix":"10.1016","volume":"180","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9341-8937","authenticated-orcid":false,"given":"Diqi","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Frank de","family":"Hoog","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wangzhi","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brano","family":"Kusy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5822-8233","authenticated-orcid":false,"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongsheng","family":"Gao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2026.114374_b1","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"IJCV"},{"key":"10.1016\/j.patcog.2026.114374_b2","series-title":"CVPR","first-page":"3558","article-title":"Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts","author":"Changpinyo","year":"2021"},{"key":"10.1016\/j.patcog.2026.114374_b3","first-page":"25278","article-title":"LAION-5B: An open large-scale dataset for training next generation image-text models","volume":"35","author":"Schuhmann","year":"2022","journal-title":"NeurIPS"},{"key":"10.1016\/j.patcog.2026.114374_b4","series-title":"ICCV","first-page":"10012","article-title":"Swin transformer: Hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"issue":"8","key":"10.1016\/j.patcog.2026.114374_b5","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"Radford","year":"2019","journal-title":"OpenAI Blog"},{"key":"10.1016\/j.patcog.2026.114374_b6","article-title":"Language models are few-shot learners","author":"Tom Brown","year":"2020","journal-title":"NeurIPS"},{"key":"10.1016\/j.patcog.2026.114374_b7","first-page":"19523","article-title":"Beyond neural scaling laws: beating power law scaling via data pruning","volume":"35","author":"Sorscher","year":"2022","journal-title":"NeurIPS"},{"key":"10.1016\/j.patcog.2026.114374_b8","series-title":"ICLR","article-title":"A constructive prediction of the generalization error across scales","author":"Rosenfeld","year":"2020"},{"key":"10.1016\/j.patcog.2026.114374_b9","doi-asserted-by":"crossref","unstructured":"M.A. Gordon, K. Duh, J. Kaplan, Data and parameter scaling laws for neural machine translation, in: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021, pp. 5915\u20135922.","DOI":"10.18653\/v1\/2021.emnlp-main.478"},{"key":"10.1016\/j.patcog.2026.114374_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.107899","article-title":"Pruning by explaining: A novel criterion for deep neural network pruning","volume":"115","author":"Yeom","year":"2021","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114374_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107468","article-title":"Goal driven network pruning for object recognition","volume":"110","author":"Kaplan","year":"2021","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114374_b12","series-title":"ICLR","article-title":"Moderate coreset: A universal method of data selection for real-world data-efficient deep learning","author":"Xia","year":"2022"},{"key":"10.1016\/j.patcog.2026.114374_b13","series-title":"ICLR","article-title":"Dataset pruning: Reducing training data by examining generalization influence","author":"Yang","year":"2023"},{"key":"10.1016\/j.patcog.2026.114374_b14","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2025.111948","article-title":"DDS-NAS: Dynamic data selection within neural architecture search via on-line hard example mining applied to image classification","volume":"169","author":"Poyser","year":"2026","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114374_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110385","article-title":"BALQUE: Batch active learning by querying unstable examples with calibrated confidence","volume":"151","author":"Han","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114374_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110392","article-title":"Embrace sustainable AI: dynamic data subset selection for image classification","volume":"151","author":"Yin","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2026.114374_b17","first-page":"20596","article-title":"Deep learning on a data diet: Finding important examples early in training","volume":"34","author":"Paul","year":"2021","journal-title":"NeurIPS"},{"key":"10.1016\/j.patcog.2026.114374_b18","series-title":"An empirical study of example forgetting during deep neural network learning","author":"Toneva","year":"2018"},{"key":"10.1016\/j.patcog.2026.114374_b19","article-title":"Trivial or impossible\u2013dichotomous data difficulty masks model differences (on ImageNet and beyond)","author":"Meding","year":"2022","journal-title":"ICLR"},{"key":"10.1016\/j.patcog.2026.114374_b20","series-title":"ICML","first-page":"1121","article-title":"Herding dynamical weights to learn","author":"Welling","year":"2009"},{"key":"10.1016\/j.patcog.2026.114374_b21","first-page":"17044","article-title":"Identifying mislabeled data using the area under the margin ranking","volume":"33","author":"Pleiss","year":"2020","journal-title":"NeurIPS"},{"key":"10.1016\/j.patcog.2026.114374_b22","series-title":"ICML","article-title":"Mind the boundary: Coreset selection via reconstructing the decision boundary","author":"Yang","year":"2024"},{"key":"10.1016\/j.patcog.2026.114374_b23","first-page":"2881","article-title":"What neural networks memorize and why: Discovering the long tail via influence estimation","volume":"33","author":"Feldman","year":"2020","journal-title":"NeurIPS"},{"key":"10.1016\/j.patcog.2026.114374_b24","series-title":"CVPR","first-page":"26223","article-title":"Spanning training progress: Temporal dual-depth scoring (tdds) for enhanced dataset pruning","author":"Zhang","year":"2024"},{"key":"10.1016\/j.patcog.2026.114374_b25","article-title":"Data pruning via moving-one-sample-out","volume":"36","author":"Tan","year":"2024","journal-title":"NeurIPS"},{"key":"10.1016\/j.patcog.2026.114374_b26","series-title":"ICML","first-page":"34533","article-title":"Provable data subset selection for efficient neural networks training","author":"Tukan","year":"2023"},{"key":"10.1016\/j.patcog.2026.114374_b27","doi-asserted-by":"crossref","unstructured":"F. Xu, S. Wang, J. Zhang, C. Sun, H. Tang, L. Zhang, UNSEEN: Enhancing dataset pruning from a generalization perspective, 40 (32) (2026) 27224\u201327232.","DOI":"10.1609\/aaai.v40i32.39938"},{"key":"10.1016\/j.patcog.2026.114374_b28","series-title":"Reinforcement learning-guided data selection via redundancy assessment","first-page":"1004","author":"Yang","year":"2025"},{"key":"10.1016\/j.patcog.2026.114374_b29","series-title":"Class-proportional coreset selection for difficulty-separable data","first-page":"6869","author":"Tsai","year":"2025"},{"issue":"4","key":"10.1016\/j.patcog.2026.114374_b30","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1007\/s12532-020-00179-2","article-title":"OSQP: an operator splitting solver for quadratic programs","volume":"12","author":"Stellato","year":"2020","journal-title":"Math. Program. Comput."},{"key":"10.1016\/j.patcog.2026.114374_b31","series-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"10.1016\/j.patcog.2026.114374_b32","series-title":"Tiny ImageNet visual recognition challenge","first-page":"3","author":"Le","year":"2015"},{"key":"10.1016\/j.patcog.2026.114374_b33","series-title":"CVPR","first-page":"248","article-title":"ImageNet: A large-scale hierarchical image database","author":"Deng","year":"2009"},{"key":"10.1016\/j.patcog.2026.114374_b34","series-title":"ECCV","first-page":"116","article-title":"ShuffleNet V2: Practical guidelines for efficient CNN architecture design","author":"Ma","year":"2018"},{"key":"10.1016\/j.patcog.2026.114374_b35","series-title":"CVPR","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"key":"10.1016\/j.patcog.2026.114374_b36","series-title":"CVPR","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"key":"10.1016\/j.patcog.2026.114374_b37","series-title":"ICML","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","author":"Tan","year":"2019"},{"key":"10.1016\/j.patcog.2026.114374_b38","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"NeurIPS"},{"issue":"6","key":"10.1016\/j.patcog.2026.114374_b39","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1037\/h0071325","article-title":"Analysis of a complex of statistical variables into principal components","volume":"24","author":"Hotelling","year":"1933","journal-title":"J. Educ. Psychol."},{"key":"10.1016\/j.patcog.2026.114374_b40","series-title":"NeurIPS","first-page":"8024","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"Paszke","year":"2019"},{"key":"10.1016\/j.patcog.2026.114374_b41","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jpdc.2020.05.021","article-title":"GPU acceleration of ADMM for large-scale quadratic programming","volume":"144","author":"Schubiger","year":"2020","journal-title":"J. Parallel Distrib. Comput."},{"key":"10.1016\/j.patcog.2026.114374_b42","series-title":"Towards deep learning models resistant to adversarial attacks","author":"Madry","year":"2017"},{"key":"10.1016\/j.patcog.2026.114374_b43","series-title":"Explaining and harnessing adversarial examples","author":"Goodfellow","year":"2014"}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326013397?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320326013397?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T12:47:36Z","timestamp":1788526056000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320326013397"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":43,"alternative-id":["S0031320326013397"],"URL":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114374","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DBCore: Shaping generalizable decision boundaries for coreset selection","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2026.114374","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"114374"}}