{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T08:22:45Z","timestamp":1776932565683,"version":"3.51.2"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/tpami.2022.3188807","type":"journal-article","created":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T19:35:03Z","timestamp":1657136103000},"page":"1-13","source":"Crossref","is-referenced-by-count":11,"title":["Contrastive Active Learning Under Class Distribution Mismatch"],"prefix":"10.1109","author":[{"given":"Pan","family":"Du","sequence":"first","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}]},{"given":"Hui","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Statistics, Renmin University of China, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9292-4371","authenticated-orcid":false,"given":"Suyun","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}]},{"given":"Shuwen","family":"Chai","sequence":"additional","affiliation":[{"name":"School of Statistics, Renmin University of China, Beijing, China"}]},{"given":"Hong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0089-1045","authenticated-orcid":false,"given":"Cuiping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information, Renmin University of China, Beijing, China"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref2","article-title":"Active learning literature survey","author":"Settles","year":"2009"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2063576.2063711"},{"key":"ref4","first-page":"3897","article-title":"Safe deep semi-supervised learning for unseen-class unlabeled data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Guo"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2015.06.004"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i8.16873"},{"key":"ref7","first-page":"3239","article-title":"Realistic evaluation of deep semi-supervised learning algorithms","volume-title":"Proc. 32nd Int. Conf. Neural Inf. Process. Syst.","author":"Oliver"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5763"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00880"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00607"},{"key":"ref11","first-page":"728","article-title":"Latent structured active learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Luo"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-55860-335-6.50026-X"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206627"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/11871842_40"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00018"},{"key":"ref16","first-page":"1183","article-title":"Deep Bayesian active learning with image data","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gal"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015349"},{"key":"ref18","article-title":"Active learning for convolutional neural networks: A core-set approach","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Sener"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2700408"},{"key":"ref20","first-page":"892","article-title":"Active learning by querying informative and representative examples","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Huang"},{"key":"ref21","article-title":"Deep batch active learning by diverse, uncertain gradient lower bounds","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Ash"},{"key":"ref22","article-title":"Generative adversarial active learning","author":"Zhu","year":"2017"},{"key":"ref23","first-page":"6295","article-title":"Bayesian generative active deep learning","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Tran"},{"key":"ref24","first-page":"2794","article-title":"A Bayesian data augmentation approach for learning deep models","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Tran"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3092833"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.3390\/technologies9010002"},{"key":"ref27","article-title":"Representation learning with contrastive predictive coding","author":"Oord","year":"2018"},{"key":"ref28","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref29","first-page":"11839","article-title":"CSI: Novelty detection via contrastive learning on distributionally shifted instances","volume-title":"Proc. 34th Conf. Neural Inf. Process. Syst.","volume":"33","author":"Tack"},{"key":"ref30","article-title":"Learning deep representations by mutual information estimation and maximization","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hjelm"},{"key":"ref31","article-title":"Learning representations by maximizing mutual information across views","author":"Bachman","year":"2019"},{"key":"ref32","article-title":"Temporal ensembling for semi-supervised learning","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Laine"},{"key":"ref33","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Tarvainen"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5268-1"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2836-1"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3031549"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-019-05855-6"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2005.01.012"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475516"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3116948"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-0865-5_26"},{"key":"ref43","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.42"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2723009"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10599-4_29"},{"key":"ref47","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.211"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"ref51","first-page":"4804","article-title":"Beyond synthetic noise: Deep learning on controlled noisy labels","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Jiang"},{"key":"ref52","article-title":"Supervised contrastive learning","author":"Khosla","year":"2020"},{"key":"ref53","first-page":"3630","article-title":"Matching networks for one shot learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Vinyals"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/4359286\/09816025.pdf?arnumber=9816025","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T04:36:13Z","timestamp":1706762173000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9816025\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/tpami.2022.3188807","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}