{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T23:47:25Z","timestamp":1771458445526,"version":"3.50.1"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"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":["61572393"],"award-info":[{"award-number":["61572393"]}],"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":["11671317"],"award-info":[{"award-number":["11671317"]}],"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":["61877049"],"award-info":[{"award-number":["61877049"]}],"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":["61976174"],"award-info":[{"award-number":["61976174"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Cybern."],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1109\/tcyb.2020.3000480","type":"journal-article","created":{"date-parts":[[2020,7,15]],"date-time":"2020-07-15T21:02:47Z","timestamp":1594846967000},"page":"2491-2504","source":"Crossref","is-referenced-by-count":12,"title":["Toward a Controllable Disentanglement Network"],"prefix":"10.1109","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7914-3252","authenticated-orcid":false,"given":"Zengjie","family":"Song","sequence":"first","affiliation":[{"name":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4023-419X","authenticated-orcid":false,"given":"Oluwasanmi","family":"Koyejo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Illinois at Urbana&#x2013;Champaign, Urbana, IL, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8395-1180","authenticated-orcid":false,"given":"Jiangshe","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistics, Xi&#x2019;an Jiaotong University, Xi&#x2019;an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_43"},{"key":"ref38","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref33","first-page":"3111","article-title":"Distributed representations of words and phrases and their compositionality","author":"mikolov","year":"2013","journal-title":"Proc Neural Inf Process Syst (NIPS)"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.131"},{"key":"ref31","author":"mirza","year":"2014","journal-title":"Conditional generative adversarial nets"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1214\/009053607000000505"},{"key":"ref37","first-page":"694","article-title":"Perceptual losses for real-time style transfer and super-resolution","author":"johnson","year":"2016","journal-title":"Proc Eur Conf Comput Vis (ECCV)"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.265"},{"key":"ref35","first-page":"2226","article-title":"Improved techniques for training GANs","author":"salimans","year":"2016","journal-title":"Proc Neural Inf Process Syst (NIPS)"},{"key":"ref34","first-page":"4114","article-title":"Challenging common assumptions in the unsupervised learning of disentangled representations","author":"locatello","year":"2019","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref10","first-page":"1","article-title":"Discovering hidden factors of variation in deep networks","author":"cheung","year":"2015","journal-title":"Proc Int Conf Learn Represent (ICLR) Workshop"},{"key":"ref40","first-page":"5767","article-title":"Improved training of Wasserstein GANs","author":"gulrajani","year":"2017","journal-title":"Proc Neural Inf Process Syst (NIPS)"},{"key":"ref11","first-page":"1","article-title":"Disentanglement by penalizing correlation","author":"k\u00e5geb\u00e4ck","year":"2017","journal-title":"Proc Neural Inf Process Syst (NIPS) Workshop"},{"key":"ref12","first-page":"1","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2014","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref13","first-page":"1278","article-title":"Stochastic backpropagation and approximate inference in deep generative models","author":"rezende","year":"2014","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref14","first-page":"1","article-title":"Beta-VAE: Learning basic visual concepts with a constrained variational framework","author":"higgins","year":"2017","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref15","first-page":"1","article-title":"Variational inference of disentangled latent concepts from unlabeled observations","author":"kumar","year":"2018","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref16","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Neural Inf Process Syst (NIPS)"},{"key":"ref17","first-page":"1","article-title":"Invertible conditional GANs for image editing","author":"perarnau","year":"2016","journal-title":"Proc Neural Inf Process Syst (NIPS) Workshop"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.141"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref28","first-page":"2172","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","author":"chen","year":"2016","journal-title":"Proc Neural Inf Process Syst (NIPS)"},{"key":"ref4","first-page":"5967","article-title":"Fader networks: Manipulating images by sliding attributes","author":"lample","year":"2017","journal-title":"Proc Neural Inf Process Syst (NIPS)"},{"key":"ref27","first-page":"2539","article-title":"Deep convolutional inverse graphics network","author":"kulkarni","year":"2015","journal-title":"Proc Neural Inf Process Syst (NIPS)"},{"key":"ref3","first-page":"708","article-title":"Learning disentangled joint continuous and discrete representations","author":"dupont","year":"2018","journal-title":"Proc NeurIPS"},{"key":"ref6","first-page":"1","article-title":"Deep variational information bottleneck","author":"alemi","year":"2017","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00018"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1017\/S0140525X16001837"},{"key":"ref8","first-page":"1480","article-title":"DARLA: Improving zero-shot transfer in reinforcement learning","author":"higgins","year":"2017","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref7","first-page":"2615","article-title":"Isolating sources of disentanglement in variational autoencoders","author":"chen","year":"2018","journal-title":"Proc NeurIPS"},{"key":"ref2","author":"ridgeway","year":"2016","journal-title":"A survey of inductive biases for factorial representation-learning"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1992.4.6.863"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref46","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","author":"kingma","year":"2014","journal-title":"Proc Neural Inf Process Syst (NIPS)"},{"key":"ref20","first-page":"472","article-title":"Soft-gated warping-GAN for pose-guided person image synthesis","author":"dong","year":"2018","journal-title":"Proc NeurIPS"},{"key":"ref45","first-page":"1","article-title":"Multi-scale structural similarity for image quality assessment","author":"wang","year":"2003","journal-title":"Proc IEEE Asilomar Conf Signals Syst Comput"},{"key":"ref22","first-page":"1558","article-title":"Autoencoding beyond pixels using a learned similarity metric","author":"larsen","year":"2016","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref21","first-page":"1","article-title":"Adversarial autoencoders","author":"makhzani","year":"2016","journal-title":"Proc Int Conf Learn Represent (ICLR) Workshop"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref24","first-page":"1857","article-title":"Learning to discover cross-domain relations with generative adversarial networks","author":"kim","year":"2017","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref41","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref23","first-page":"1","article-title":"Adversarially learned inference","author":"dumoulin","year":"2017","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref44","first-page":"1","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2016","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref26","first-page":"1","article-title":"Latent constraints: Learning to generate conditionally from unconditional generative models","author":"engel","year":"2018","journal-title":"Proc Int Conf Learn Represent (ICLR)"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref25","first-page":"1","article-title":"Semantically decomposing the latent spaces of generative adversarial networks","author":"donahue","year":"2018","journal-title":"Proc Int Conf Learn Represent (ICLR)"}],"container-title":["IEEE Transactions on Cybernetics"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6221036\/9749993\/09141386.pdf?arnumber=9141386","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,23]],"date-time":"2022-05-23T20:26:07Z","timestamp":1653337567000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9141386\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4]]},"references-count":46,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tcyb.2020.3000480","relation":{},"ISSN":["2168-2267","2168-2275"],"issn-type":[{"value":"2168-2267","type":"print"},{"value":"2168-2275","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4]]}}}