{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:21:09Z","timestamp":1740169269874,"version":"3.37.3"},"reference-count":68,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"Korean Government through the MSIT","doi-asserted-by":"publisher","award":["NRF-2019R1A2C1006706"],"award-info":[{"award-number":["NRF-2019R1A2C1006706"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Information and Communications Technology Planning and Evaluation"},{"name":"Korean Government through the MSIT","award":["2020-0-01389"],"award-info":[{"award-number":["2020-0-01389"]}]},{"DOI":"10.13039\/501100002635","name":"Inha University Research Grant","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002635","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3122834","type":"journal-article","created":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T20:40:02Z","timestamp":1635280802000},"page":"144699-144712","source":"Crossref","is-referenced-by-count":1,"title":["Controllable Image Dataset Construction Using Conditionally Transformed Inputs in Generative Adversarial Networks"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2594-8327","authenticated-orcid":false,"given":"Farkhod","family":"Makhmudkhujaev","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9526-7549","authenticated-orcid":false,"given":"Junseok","family":"Kwon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4774-7841","authenticated-orcid":false,"given":"In Kyu","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"ref38","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"arXiv 1502 03167"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref32","first-page":"1486","article-title":"Deep generative image models using a Laplacian pyramid of adversarial networks","author":"denton","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref31","first-page":"6147e","article-title":"Multi-view frontal face image generation: A survey","author":"ning","year":"2020","journal-title":"Concurrency Comput Pract Exper"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_10"},{"key":"ref37","first-page":"6594","article-title":"Modulating early visual processing by language","author":"de vries","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref36","first-page":"1330","article-title":"Twin auxiliary classifiers GAN","author":"gong","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","first-page":"1","article-title":"Spectral normalization for generative adversarial networks","author":"miyato","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref34","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref60","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":"ref62","first-page":"5767","article-title":"Improved training of Wasserstein GANs","author":"gulrajani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref61","first-page":"723","article-title":"A kernel two-sample test","volume":"13","author":"gretton","year":"2012","journal-title":"J Mach Learn Res"},{"key":"ref63","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2013","journal-title":"arXiv 1312 6114"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref64","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"ref27","first-page":"465","article-title":"Toward multimodal image-to-image translation","author":"zhu","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_3"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00152"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.723"},{"article-title":"Representational disentanglement for multi-domain image completion","year":"2019","author":"shen","key":"ref67"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00569"},{"key":"ref2","first-page":"1","article-title":"Progressive growing of GANs for improved quality, stability, and variation","author":"karras","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref1","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref20","first-page":"5929","article-title":"Deep generative models for distribution-preserving lossy compression","author":"tschannen","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref22","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","author":"odena","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref21","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014","journal-title":"arXiv 1411 1784"},{"key":"ref24","article-title":"Invertible conditional GANs for image editing","author":"perarnau","year":"2016","journal-title":"arXiv 1611 06355"},{"key":"ref23","first-page":"4790","article-title":"Conditional image generation with PixelCNN decoders","author":"van den oord","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref26","first-page":"1","article-title":"On self modulation for generative adversarial networks","author":"chen","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_18"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1080\/02699930903485076"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2009.08.002"},{"key":"ref59","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume":"25","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref57","first-page":"6626","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","author":"heusel","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref55","first-page":"2234","article-title":"Improved techniques for training GANs","author":"salimans","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2011.17"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00235"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00779"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00244"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5792"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00821"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58539-6_44"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2020.2978633"},{"key":"ref17","first-page":"1","article-title":"A learned representation for artistic style","author":"dumoulin","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00193"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2942480"},{"key":"ref4","first-page":"1","article-title":"Large scale GAN training for high fidelity natural image synthesis","author":"brock","year":"2019","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref3","first-page":"1","article-title":"cGANs with projection discriminator","author":"miyato","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2019.2908352"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref8","first-page":"284","article-title":"Blind super-resolution kernel estimation using an internal-GAN","author":"bell-kligler","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.3758\/BRM.42.1.351"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00467"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref45","first-page":"1","article-title":"Deep multi-scale video prediction beyond mean square error","author":"mathieu","year":"2016","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2985086"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref41","first-page":"613","article-title":"AgingmapGAN (AMGAN): High-resolution controllable face aging with spatially-aware conditional GANs","author":"despois","year":"2020","journal-title":"Proc Eur Conf Comput Vis Workshops"},{"key":"ref44","first-page":"109","article-title":"Variational conditional GAN for fine-grained controllable image generation","author":"hu","year":"2019","journal-title":"Proc Asian Conf Mach Learn"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2856256"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09585485.pdf?arnumber=9585485","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T22:22:43Z","timestamp":1645482163000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9585485\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":68,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3122834","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2021]]}}}