{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,24]],"date-time":"2025-08-24T00:02:21Z","timestamp":1755993741341,"version":"3.44.0"},"reference-count":33,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"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":["U19B2044","61836011"],"award-info":[{"award-number":["U19B2044","61836011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"International Conference on Pattern Recognition, 2020"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Artif. Intell."],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1109\/tai.2021.3071642","type":"journal-article","created":{"date-parts":[[2021,6,30]],"date-time":"2021-06-30T15:31:39Z","timestamp":1625067099000},"page":"58-70","source":"Crossref","is-referenced-by-count":13,"title":["Interpreting the Latent Space of GANs via Measuring Decoupling"],"prefix":"10.1109","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9484-2310","authenticated-orcid":false,"given":"Ziqiang","family":"Li","sequence":"first","affiliation":[{"name":"CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9023-0066","authenticated-orcid":false,"given":"Rentuo","family":"Tao","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9902-5723","authenticated-orcid":false,"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongjing","family":"Niu","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingdao","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Suzhou University, Suzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2332-3959","authenticated-orcid":false,"given":"Bin","family":"Li","sequence":"additional","affiliation":[{"name":"CAS Key Laboratory of Technology in Geo-Spatial Information Processing and Application Systems, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"article-title":"LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop","year":"2015","author":"yu","key":"ref33"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.594"},{"article-title":"Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms","year":"2017","author":"xiao","key":"ref31"},{"key":"ref30","article-title":"GAN dissection: Visualizing and understanding generative adversarial networks","author":"bau","year":"2018","journal-title":"Int Conf Learn Representations"},{"article-title":"Conditional generative adversarial nets","year":"2014","author":"mirza","key":"ref10"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00584"},{"key":"ref12","first-page":"2172","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","author":"chen","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2019.2953753"},{"key":"ref14","first-page":"9786","article-title":"Unsupervised discovery of interpretable directions in the GAN latent space","author":"voynov","year":"0","journal-title":"Proc 37th Int Conf Mach Learn"},{"key":"ref15","first-page":"2579","article-title":"Visualizing data using T-SNE","volume":"9","author":"maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref16","first-page":"5767","article-title":"Improved training of Wasserstein GANs","author":"gulrajani","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01243"},{"article-title":"Direct adversarial training for GANs","year":"2020","author":"li","key":"ref18"},{"article-title":"Geometric GAN","year":"2017","author":"lim","key":"ref19"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2941272"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref27","article-title":"On the “steerability” of generative adversarial networks","author":"jahanian","year":"2019","journal-title":"Int Conf Learn Representations"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00453"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref29","article-title":"Discovering interpretable GAN controls","volume":"33","author":"h\u00e4rk\u00f6nen","year":"2020","journal-title":"Adv in Neural Info Proc Syst"},{"key":"ref5","first-page":"5967","article-title":"Fader networks: Manipulating images by sliding attributes","author":"lample","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2017.8296650"},{"key":"ref7","article-title":"cGANs with projection discriminator","author":"miyato","year":"2018","journal-title":"Int Conf Learn Representations"},{"key":"ref2","article-title":"Large scale GAN training for high fidelity natural image synthesis","author":"brock","year":"2018","journal-title":"Int Conf Learn Representations"},{"article-title":"Semantically decomposing the latent spaces of generative adversarial networks","year":"2017","author":"donahue","key":"ref9"},{"key":"ref1","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref20","first-page":"818","article-title":"c","author":"zeiler","year":"0","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref22","first-page":"1135","author":"ribeiro","year":"0","journal-title":"Proc ACM SIGKDD Int Conf Knowledge Discovery and Data Mining"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299155"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00642"},{"key":"ref23","first-page":"2124","article-title":"Identifying unknown unknowns in the open world: Representations and policies for guided exploration","author":"lakkaraju","year":"0","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00926"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00920"}],"container-title":["IEEE Transactions on Artificial Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9078688\/9460749\/09399843.pdf?arnumber=9399843","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T01:09:54Z","timestamp":1755911394000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9399843\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2]]},"references-count":33,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tai.2021.3071642","relation":{},"ISSN":["2691-4581"],"issn-type":[{"type":"electronic","value":"2691-4581"}],"subject":[],"published":{"date-parts":[[2021,2]]}}}