{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T06:25:51Z","timestamp":1771050351255,"version":"3.50.1"},"reference-count":41,"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"}],"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\/501100003621","name":"Korea Government","doi-asserted-by":"publisher","award":["NRF-2019R1A2C1006706"],"award-info":[{"award-number":["NRF-2019R1A2C1006706"]}],"id":[{"id":"10.13039\/501100003621","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Institute of Information and Communications Technology Planning and Evaluation (IITP) grant"},{"DOI":"10.13039\/100018202","name":"Korea Government (MSIT), Artificial Intelligence Convergence Research Center","doi-asserted-by":"publisher","award":["2020-0-01389"],"award-info":[{"award-number":["2020-0-01389"]}],"id":[{"id":"10.13039\/100018202","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Artificial Intelligence Innovation Hub","award":["2021-0-02068"],"award-info":[{"award-number":["2021-0-02068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3135637","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T20:40:34Z","timestamp":1639514434000},"page":"168404-168414","source":"Crossref","is-referenced-by-count":1,"title":["Generative Adversarial Networks With Attention Mechanisms at Every Scale"],"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\/0000-0003-4774-7841","authenticated-orcid":false,"given":"In Kyu","family":"Park","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","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":"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\/CVPR42600.2020.00821"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58568-6_25"},{"key":"ref30","first-page":"1","article-title":"U-GAT-IT: Unsupervised generative attentional networks with adaptive layer-instance normalization for image-to-image translation","author":"kim","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref37","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref36","first-page":"1","article-title":"Automatic differentiation in pytorch","author":"paszke","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref35","article-title":"Exact solutions to the nonlinear dynamics of learning in deep linear neural networks","author":"saxe","year":"2013","journal-title":"arXiv 1312 6120"},{"key":"ref34","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref10","first-page":"3481","article-title":"Which training methods for GANs do actually converge?","author":"mescheder","year":"2018","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref11","first-page":"1","article-title":"Consistency regularization for generative adversarial networks","author":"zhang","year":"2020","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref40","first-page":"2234","article-title":"Improved techniques for training GANs","author":"salimans","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref12","first-page":"6249","article-title":"Progressive augmentation of gans","author":"zhang","year":"2019","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref13","first-page":"7354","article-title":"Self-attention generative adversarial networks","author":"zhang","year":"2019","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01454"},{"key":"ref15","article-title":"Conditional generative adversarial nets","author":"mirza","year":"2014","journal-title":"arXiv 1411 1784"},{"key":"ref16","first-page":"1","article-title":"Energy-based generative adversarial network","author":"zhao","year":"2017","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref17","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref19","first-page":"1","article-title":"cGANs with projection discriminator","author":"miyato","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00252"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01216-8_11"},{"key":"ref3","first-page":"1","article-title":"Large scale GAN training for high fidelity natural image synthesis","author":"brock","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01249-6_50"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00461"},{"key":"ref8","first-page":"5767","article-title":"Improved training of Wasserstein GANs","author":"gulrajani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00823"},{"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":"ref9","first-page":"1","article-title":"Spectral normalization for generative adversarial networks","author":"miyato","year":"2018","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref20","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"simonyan","year":"2013","journal-title":"arXiv 1312 6034"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.683"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09650851.pdf?arnumber=9650851","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,28]],"date-time":"2022-03-28T21:19:38Z","timestamp":1648502378000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9650851\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":41,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3135637","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}