{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:35:44Z","timestamp":1762868144712,"version":"3.37.3"},"reference-count":54,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["CMMI-1921646"],"award-info":[{"award-number":["CMMI-1921646"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2021,11,1]]},"DOI":"10.1109\/tpami.2020.2993221","type":"journal-article","created":{"date-parts":[[2020,5,11]],"date-time":"2020-05-11T19:44:14Z","timestamp":1589226254000},"page":"3770-3781","source":"Crossref","is-referenced-by-count":9,"title":["Active Image Synthesis for Efficient Labeling"],"prefix":"10.1109","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6053-9302","authenticated-orcid":false,"given":"Jialei","family":"Chen","sequence":"first","affiliation":[]},{"given":"Yujia","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Kan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Chuck","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Mani A.","family":"Vannan","sequence":"additional","affiliation":[]},{"given":"Ben","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Qian","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.2014.881749"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1002\/j.1538-7305.1948.tb01338.x"},{"article-title":"Generative adversarial active learning","year":"2017","author":"zhu","key":"ref33"},{"article-title":"Adversarial active learning for deep networks: A margin based approach","year":"2018","author":"ducoffe","key":"ref32"},{"key":"ref31","first-page":"1183","article-title":"Deep Bayesian active learning with image data","author":"gal","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.2200\/S00429ED1V01Y201207AIM018"},{"key":"ref37","volume":"338","author":"villani","year":"2008","journal-title":"Optimal Transport Old and New"},{"journal-title":"A Probability Path","year":"2013","author":"resnick","key":"ref36"},{"key":"ref35","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"2017"},{"article-title":"Few-shot learning: A survey","year":"2019","author":"wang","key":"ref34"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1037\/11774-000"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2014.2330900"},{"article-title":"Active learning literature survey","year":"2009","author":"settles","key":"ref29"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02478259"},{"key":"ref20","first-page":"10 215","article-title":"Glow: Generative flow with invertible 1x1 convolutions","author":"kingma","year":"2018","journal-title":"Proc Int Conf Neural Inf Process"},{"article-title":"Adversarial feature learning","year":"2016","author":"donahue","key":"ref22"},{"key":"ref21","article-title":"Adversarially learned inference","volume":"1050","author":"dumoulin","year":"2016"},{"key":"ref24","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","author":"odena","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"article-title":"Conditional generative adversarial nets","year":"2014","author":"mirza","key":"ref23"},{"key":"ref26","first-page":"3320","article-title":"How transferable are features in deep neural networks?","author":"yosinski","year":"2014","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ISBI.2018.8363576"},{"year":"2017","key":"ref50","article-title":"Fashion MNIST"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2018.10.045"},{"article-title":"Function-on-function kriging, with applications to 3D printing of aortic tissues","year":"2019","author":"chen","key":"ref53"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref10","volume":"206","author":"anderson","year":"1995","journal-title":"Computational Fluid Dynamics"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1038\/38686"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1214\/17-AOS1629"},{"key":"ref12","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.5244\/C.28.6"},{"article-title":"The effectiveness of data augmentation in image classification using deep learning","year":"0","author":"wang","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00928-1_61"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref18","article-title":"Auto-encoding variational bayes","volume":"1050","author":"kingma","year":"2014"},{"article-title":"Towards deeper understanding of variational autoencoding models","year":"2017","author":"zhao","key":"ref19"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2010.5537907"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmsy.2018.01.003"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref8","article-title":"How many samples are needed to learn a convolutional neural network?","volume":"1050","author":"du","year":"2018"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"article-title":"Adaptive design for Gaussian process regression under censoring","year":"2019","author":"chen","key":"ref49"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/S0735-1097(96)00563-3"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcmg.2017.04.005"},{"article-title":"The MNIST database of handwritten digits","year":"1998","author":"lecun","key":"ref48"},{"article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","year":"2017","author":"xiao","key":"ref47"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.2113\/gsecongeo.58.8.1246"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1137\/1109020"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1016\/j.addma.2018.10.007"},{"key":"ref43","volume":"36","author":"zienkiewicz","year":"1977","journal-title":"The Finite Element Method"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/34\/9556112\/9091327-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9556112\/09091327.pdf?arnumber=9091327","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:49:26Z","timestamp":1652194166000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9091327\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,1]]},"references-count":54,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2020.2993221","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"type":"print","value":"0162-8828"},{"type":"electronic","value":"2160-9292"},{"type":"electronic","value":"1939-3539"}],"subject":[],"published":{"date-parts":[[2021,11,1]]}}}