{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:23:39Z","timestamp":1780511019143,"version":"3.54.1"},"reference-count":66,"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\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61671354"],"award-info":[{"award-number":["61671354"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Radar Signal Processing Laboratory","award":["KGJ202206"],"award-info":[{"award-number":["KGJ202206"]}]},{"DOI":"10.13039\/501100013314","name":"Higher Education Discipline Innovation Project","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/jstars.2023.3239633","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T19:08:16Z","timestamp":1674587296000},"page":"1785-1801","source":"Crossref","is-referenced-by-count":38,"title":["Attribute-Guided Generative Adversarial Network With Improved Episode Training Strategy for Few-Shot SAR Image Generation"],"prefix":"10.1109","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9735-0415","authenticated-orcid":false,"given":"Yuanshuang","family":"Sun","sequence":"first","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7993-2809","authenticated-orcid":false,"given":"Yinghua","family":"Wang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Hu","sequence":"additional","affiliation":[{"name":"Science and Technology on Electromagnetic Scattering Laboratory, Beijing Institute of Environmental Features, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Huang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4046-163X","authenticated-orcid":false,"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5743-5318","authenticated-orcid":false,"given":"Siyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0486-8208","authenticated-orcid":false,"given":"Chen","family":"Zhang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"ref2","article-title":"Conditional generative adversarial nets","author":"Mirza","year":"2014"},{"key":"ref3","article-title":"Progressive growing of GANs for improved quality, stability, and variation","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Karras","year":"2018"},{"key":"ref4","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Odena","year":"2017"},{"key":"ref5","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Arjovsky","year":"2017"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3386252"},{"key":"ref8","article-title":"Awesome few-shot image generation"},{"key":"ref9","article-title":"FIGR: Few-shot image generation with reptile","author":"Cloutre","year":"2019"},{"key":"ref10","article-title":"DAWSON: A domain adaptive few shot generation framework","author":"Liang","year":"2020"},{"key":"ref11","article-title":"Augmentation-interpolative autoencoders for unsupervised few-shot image generation","author":"Wertheimer","year":"2020"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3394171.3413561"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICME46284.2020.9102917"},{"key":"ref14","first-page":"670","article-title":"Few-shot generative modelling with generative matching networks","volume-title":"Proc. Int. Conf. Artif. Intell. Statist.","author":"Bartunov","year":"2018"},{"key":"ref15","article-title":"Data augmentation generative adversarial networks","author":"Antoniou","year":"2017"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19787-1_15"},{"key":"ref17","article-title":"On first-order meta-learning algorithms","author":"Nichol","year":"2018"},{"key":"ref18","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Finn","year":"2017"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2699196"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2019.8898922"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2019.2957453"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2907728"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.3390\/rs13193864"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2021.3086817"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.3390\/rs13193939"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1802.05957"},{"key":"ref27","article-title":"Research on SAR image generation models","author":"Huang","year":"2021"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00284"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00935"},{"key":"ref32","first-page":"15885","article-title":"Few-shot image generation with elastic weight consolidation","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Li","year":"2020"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01060"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3079209"},{"key":"ref35","first-page":"3637","article-title":"Matching networks for one shot learning","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Vinyals","year":"2016"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295163"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00981"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS.2018.8517866"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2017.2717486"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2018.8462109"},{"key":"ref41","first-page":"160","article-title":"Refining simulated SAR images with conditional GAN to train ATR algorithms","volume-title":"Proc. Actes de la Conf. CAID","author":"Camus","year":"2020"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1117\/12.2523460"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3059991"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2936897"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2022.3141389"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2016.2551720"},{"key":"ref47","doi-asserted-by":"crossref","first-page":"27:1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM. Trans. Intel. Syst. Technol."},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3166174"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.23919\/URSIGASS49373.2020.9232410"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS47720.2021.9554867"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2021.3126688"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/tgrs.2021.3058249"},{"key":"ref53","article-title":"Tensorflow-generative-model-collections"},{"key":"ref54","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Maas","year":"2013"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104425"},{"key":"ref56","first-page":"315","article-title":"Deep sparse rectifier neural networks","volume-title":"Proc. 14th Int. Conf. Artif. Intell. Statist.","author":"Glorot","year":"2011"},{"key":"ref57","article-title":"Deep learning: Technical introduction","author":"Epelbaum","year":"2018"},{"key":"ref58","article-title":"Instance normalization: The missing ingredient for fast stylization","author":"Ulyanov","year":"2016"},{"key":"ref59","article-title":"Towards principled methods for training generative adversarial networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Arjovsky","year":"2017"},{"key":"ref60","article-title":"Spectral norm regularization for improving the generalizability of deep learning","author":"Yoshida","year":"2017"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-0427(00)00413-1"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2958379"},{"key":"ref63","first-page":"3320","article-title":"How transferable are features in deep neural networks?","volume-title":"Proc. 27th Int. Conf. Neural Inf. Process. Syst.","author":"Yosinski","year":"2014"},{"key":"ref64","volume-title":"Computing in Communication Networks","author":"Fitzek","year":"2020"},{"issue":"3","key":"ref65","first-page":"351","article-title":"Efficient simulation method for high quality SAR images of complex ground vehicles","volume":"4","author":"Dong","year":"2015","journal-title":"J. Radars"},{"key":"ref66","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2015.2513754"}],"container-title":["IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/4609443\/9973430\/10025584.pdf?arnumber=10025584","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T08:20:34Z","timestamp":1707812434000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10025584\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":66,"URL":"https:\/\/doi.org\/10.1109\/jstars.2023.3239633","relation":{},"ISSN":["1939-1404","2151-1535"],"issn-type":[{"value":"1939-1404","type":"print"},{"value":"2151-1535","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}