{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T01:01:34Z","timestamp":1769043694895,"version":"3.49.0"},"reference-count":61,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"11","license":[{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,11,1]],"date-time":"2019-11-01T00:00:00Z","timestamp":1572566400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602185"],"award-info":[{"award-number":["61602185"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876208"],"award-info":[{"award-number":["61876208"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61502177"],"award-info":[{"award-number":["61502177"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Recruitment Program for Young Professionals"},{"name":"Guangdong Provincial Scientific and Technological Fund","award":["2017B090901008"],"award-info":[{"award-number":["2017B090901008"]}]},{"name":"Guangdong Provincial Scientific and Technological Fund","award":["2017A010101011"],"award-info":[{"award-number":["2017A010101011"]}]},{"name":"Guangdong Provincial Scientific and Technological Fund","award":["2017B090910005"],"award-info":[{"award-number":["2017B090910005"]}]},{"DOI":"10.13039\/501100009334","name":"Pearl River S and T Nova Program of Guangzhou","doi-asserted-by":"publisher","award":["201806010081"],"award-info":[{"award-number":["201806010081"]}],"id":[{"id":"10.13039\/501100009334","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CCF-Tencent Open Research Fund","award":["RAGR20170105"],"award-info":[{"award-number":["RAGR20170105"]}]},{"name":"Program for Guangdong Introducing Innovative and Enterpreneurial Teams","award":["2017ZT07X183"],"award-info":[{"award-number":["2017ZT07X183"]}]},{"name":"Scientific and Technological Planning on Provincial Key Research and Development","award":["2018B01010700"],"award-info":[{"award-number":["2018B01010700"]}]},{"name":"Guangdong Special Branch Plans Young Talent"},{"name":"Scientific and Technological Innovation","award":["2016TQ03X445"],"award-info":[{"award-number":["2016TQ03X445"]}]},{"name":"Guangzhou Science and Technology Planning Project","award":["2019-03-01-06-3002-0003"],"award-info":[{"award-number":["2019-03-01-06-3002-0003"]}]},{"name":"Guangzhou Tianhe District Science and Technology Planning","award":["201702YH112"],"award-info":[{"award-number":["201702YH112"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Multimedia"],"published-print":{"date-parts":[[2019,11]]},"DOI":"10.1109\/tmm.2019.2908352","type":"journal-article","created":{"date-parts":[[2019,3,29]],"date-time":"2019-03-29T18:45:06Z","timestamp":1553885106000},"page":"2726-2737","source":"Crossref","is-referenced-by-count":61,"title":["Auto-Embedding Generative Adversarial Networks For High Resolution Image Synthesis"],"prefix":"10.1109","volume":"21","author":[{"given":"Yong","family":"Guo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8732-8049","authenticated-orcid":false,"given":"Qi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4769-1526","authenticated-orcid":false,"given":"Jian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6771-3932","authenticated-orcid":false,"given":"Qingyao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinfeng","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8856-756X","authenticated-orcid":false,"given":"Mingkui","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref38","article-title":"Self-attention generative adversarial networks","author":"zhang","year":"2018"},{"key":"ref33","article-title":"Attngan: Fine-grained text to image generation with attentional generative adversarial networks","author":"huang","year":"2018"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2015.2496246"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.241"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.374"},{"key":"ref36","first-page":"6626","article-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","author":"heusel","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","article-title":"On self modulation for generative adversarial networks","author":"chen","year":"2018","journal-title":"ICLR2019"},{"key":"ref34","article-title":"Large scale GAN training for high fidelity natural image synthesis","author":"brock","year":"2018","journal-title":"ICLR2019"},{"key":"ref60","first-page":"807","article-title":"Rectified linear units improve restricted boltzmann machines","author":"nair","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref61","article-title":"Rectifier nonlinearities improve neural network acoustic models","author":"maas","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"ref29","first-page":"412","article-title":"Learning a nonlinear embedding by preserving class neighbourhood structure","author":"salakhutdinov","year":"0","journal-title":"Proc 11th Int Conf Artif Intell Statist"},{"key":"ref2","article-title":"BEGAN: Boundary equilibrium generative adversarial networks","author":"berthelot","year":"2017"},{"key":"ref1","first-page":"214","article-title":"Wasserstein GAN","author":"arjovsky","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2011.2128862"},{"key":"ref22","first-page":"2642","article-title":"Conditional image synthesis with auxiliary classifier GANs","author":"odena","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref24","article-title":"Video (language) modeling: A baseline for generative models of natural videos","author":"ranzato","year":"2014"},{"key":"ref23","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref26","article-title":"Variational approaches for auto-encoding generative adversarial networks","author":"rosca","year":"2017"},{"key":"ref25","first-page":"1060","article-title":"Generative adversarial text to image synthesis","volume":"3","author":"reed","year":"0","journal-title":"Proc 33rd Int Conf Mach Learn"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2012.2232646"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.310"},{"key":"ref59","first-page":"448","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"0","journal-title":"Proc 32nd Int Conf Mach Learn"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref57","article-title":"Energy-based generative adversarial network","author":"zhao","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.629"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00101"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00577"},{"key":"ref53","first-page":"5505","article-title":"LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop","author":"yu","year":"0","journal-title":"Proc CVPR2018"},{"key":"ref52","first-page":"11","article-title":"Extracting salient features from less data via L1-minimization","volume":"19","author":"yin","year":"0","journal-title":"SIAG\/OPT Views-and-News"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref11","article-title":"Texture synthesis with spatial generative adversarial networks","author":"jetchev","year":"0","journal-title":"Proc NIPS Workshop on Adversarial Training"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2319"},{"key":"ref12","article-title":"Progressive growing of GANs for improved quality, stability, and variation","author":"karras","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref13","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref14","first-page":"1558","article-title":"Autoencoding beyond pixels using a learned similarity metric","author":"larsen","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2687759"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref18","first-page":"2802","article-title":"Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections","author":"mao","year":"0","journal-title":"Proc 30th Int Conf Adv Neural Inf Process Syst"},{"key":"ref19","article-title":"Deep multi-scale video prediction beyond mean square error","author":"mathieu","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref4","first-page":"1486","article-title":"Deep generative image models using an laplacian pyramid of adversarial networks","author":"denton","year":"0","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2834867"},{"key":"ref6","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"0","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00824"},{"key":"ref8","first-page":"770","author":"he","year":"0","journal-title":"Proc IEEE Conf Comput Vision Pattern Recognit"},{"key":"ref7","first-page":"5769","article-title":"Improved training of wasserstein GANs","author":"gulrajani","year":"0","journal-title":"Proc 31st Int Conf Adv Neural Inf Process Syst"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.434"},{"key":"ref9","article-title":"Arbitrary facial attribute editing: Only change what you want","author":"he","year":"2017"},{"key":"ref46","article-title":"ESRGAN: Enhanced super-resolution generative adversarial networks","author":"wang","year":"2018"},{"key":"ref45","article-title":"The caltech-UCSD Birds-200-2011 Dataset","author":"wah","year":"0","journal-title":"Proceedings of ECCV Workshop"},{"key":"ref48","article-title":"Improving generative adversarial networks with denoising feature matching","author":"warde-farley","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2003.1292216"},{"key":"ref42","first-page":"1250","article-title":"Adversarial generator-encoder networks","author":"ulyanov","year":"2018","journal-title":"Proc Assoc Adv Artif Intell"},{"key":"ref41","first-page":"1349","article-title":"Texture networks: Feed-forward synthesis of textures and stylized images","author":"ulyanov","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref44","first-page":"6721?6729","article-title":"Learning a parametric embedding by preserving local structure","volume":"500","author":"van der maaten","year":"2009","journal-title":"RBM"},{"key":"ref43","first-page":"707","article-title":"Adversarial learning with local coordinate coding","author":"cao","year":"0","journal-title":"Proc Int Conf Mach Learn"}],"container-title":["IEEE Transactions on Multimedia"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6046\/8882528\/08676365.pdf?arnumber=8676365","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T20:50:17Z","timestamp":1657745417000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8676365\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11]]},"references-count":61,"journal-issue":{"issue":"11"},"URL":"https:\/\/doi.org\/10.1109\/tmm.2019.2908352","relation":{},"ISSN":["1520-9210","1941-0077"],"issn-type":[{"value":"1520-9210","type":"print"},{"value":"1941-0077","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11]]}}}