{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T10:58:58Z","timestamp":1760785138893,"version":"3.37.3"},"reference-count":49,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR202103050458"],"award-info":[{"award-number":["ZR202103050458"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008855","name":"Startup Foundation for Doctoral Research of Zaozhuang University","doi-asserted-by":"publisher","award":["1020714"],"award-info":[{"award-number":["1020714"]}],"id":[{"id":"10.13039\/501100008855","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3276875","type":"journal-article","created":{"date-parts":[[2023,5,16]],"date-time":"2023-05-16T19:48:04Z","timestamp":1684266484000},"page":"49059-49070","source":"Crossref","is-referenced-by-count":2,"title":["Low-Sample Image Classification Based on Intrinsic Consistency Loss and Uncertainty Weighting Method"],"prefix":"10.1109","volume":"11","author":[{"given":"Zhiguo","family":"Li","sequence":"first","affiliation":[{"name":"Information Construction and Service Center, Neijiang Normal University, Neijiang, China"}]},{"given":"Lingbo","family":"Li","sequence":"additional","affiliation":[{"name":"Library of Information Center, Zhejiang Technical Institute of Economics, Hangzhou, China"}]},{"given":"Xi","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Southwest Petroleum University, Chengdu, China"}]},{"given":"Jinpeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA"}]},{"given":"Nawei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, China University of Petroleum, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4704-8254","authenticated-orcid":false,"given":"Sai","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Zaozhuang University, Zaozhuang, China"}]}],"member":"263","reference":[{"key":"ref13","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1080\/21681163.2023.2179341"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref11","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"Proc 13th Eur Conf Comput Vis (ECCV)"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/TCCN.2022.3176640"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3511176.3511207"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i18.17895"},{"key":"ref18","first-page":"1","article-title":"Hyperspectral imagery classification based on contrastive learning","volume":"60","author":"hou","year":"2021","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"ref46","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref45","article-title":"Semi-supervised learning with generative adversarial networks","author":"odena","year":"2016","journal-title":"arXiv 1606 01583"},{"article-title":"Reading digits in natural images with unsupervised feature learning","year":"2011","author":"netzer","key":"ref48"},{"key":"ref47","first-page":"1","article-title":"A simple weight decay can improve generalization","volume":"4","author":"krogh","year":"1991","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref42","first-page":"1","article-title":"Triple generative adversarial nets","volume":"30","author":"li","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICMLA.2019.00137"},{"key":"ref44","article-title":"Adversarially learned inference","author":"dumoulin","year":"2016","journal-title":"arXiv 1606 00704"},{"key":"ref43","first-page":"1","article-title":"Improved techniques for training single-image GANs","volume":"29","author":"salimans","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"journal-title":"The CIFAR-10 Dataset (2014)","year":"2020","author":"krizhevsky","key":"ref49"},{"key":"ref8","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"minaee","year":"2022","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2929059"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref40","article-title":"Semi-supervised learning with context-conditional generative adversarial networks","author":"denton","year":"2016","journal-title":"arXiv 1611 06430"},{"key":"ref35","first-page":"5502","article-title":"A semantic loss function for deep learning with symbolic knowledge","author":"xu","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/s10479-005-5724-z"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-015-1916-x"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2019.108520"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29407-6_4"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.07.007"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.3390\/app12136726"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.2995518"},{"key":"ref2","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv 1704 04861"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/42.918473"},{"key":"ref39","article-title":"Reduced focal loss: 1st place solution to xView object detection in satellite imagery","author":"sergievskiy","year":"2019","journal-title":"arXiv 1903 01347"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref24","article-title":"Generate high resolution images with generative variational autoencoder","author":"sagar","year":"2020","journal-title":"arXiv 2008 10399"},{"key":"ref23","first-page":"19667","article-title":"NVAE: A deep hierarchical variational autoencoder","volume":"33","author":"vahdat","year":"2020","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref25","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014","journal-title":"arXiv 1411 1784"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-15-6759-9_11"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186150"},{"key":"ref28","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","author":"odena","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref27","article-title":"Large scale GAN training for high fidelity natural image synthesis","author":"brock","year":"2018","journal-title":"arXiv 1809 11096"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3185587"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10124880.pdf?arnumber=10124880","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T18:25:01Z","timestamp":1687199101000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10124880\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3276875","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2023]]}}}