{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T17:09:46Z","timestamp":1775754586425,"version":"3.50.1"},"reference-count":74,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Hikvision gift fund"},{"name":"NSF DMS","award":["1310391"],"award-info":[{"award-number":["1310391"]}]},{"name":"DARPA SIMPLEX","award":["N66001-15-C-4035"],"award-info":[{"award-number":["N66001-15-C-4035"]}]},{"name":"ONR MURI","award":["N00014-16-1-2007"],"award-info":[{"award-number":["N00014-16-1-2007"]}]},{"name":"DARPA ARO","award":["W911NF-16-1-0579"],"award-info":[{"award-number":["W911NF-16-1-0579"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2020,1,1]]},"DOI":"10.1109\/tpami.2018.2879081","type":"journal-article","created":{"date-parts":[[2018,11,6]],"date-time":"2018-11-06T01:59:32Z","timestamp":1541469572000},"page":"27-45","source":"Crossref","is-referenced-by-count":46,"title":["Cooperative Training of Descriptor and Generator Networks"],"prefix":"10.1109","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6818-7444","authenticated-orcid":false,"given":"Jianwen","family":"Xie","sequence":"first","affiliation":[]},{"given":"Yang","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Ruiqi","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Song-Chun","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Ying Nian","family":"Wu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2007.910956"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1016\/j.sigpro.2015.10.025"},{"key":"ref71","first-page":"53","article-title":"Dynamic texture with fourier descriptors","author":"abraham","year":"2005","journal-title":"Proc 4th Int Workshop Texture Analysis Synthesis"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-15552-9_17"},{"key":"ref74","volume":"135","author":"sutton","year":"1998","journal-title":"Introduction to Reinforcement Learning"},{"key":"ref39","first-page":"4292","article-title":"Cooperative learning of energy-based model and latent variable model via MCMC teaching","author":"xie","year":"2018","journal-title":"Proc 30th AAAI Conf Artif Intell"},{"key":"ref38","first-page":"4790","article-title":"Conditional image generation with pixelcnn decoders","author":"oord","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref33","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015","journal-title":"ArXiv 1503 02531"},{"key":"ref32","first-page":"1791","article-title":"Neural variational inference and learning in belief networks","author":"mnih","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref31","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"},{"key":"ref30","article-title":"Auto-encoding variational bayes","author":"kingma","year":"2014","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00390"},{"key":"ref36","first-page":"823","article-title":"Introspective classification with convolutional nets","author":"jin","year":"2017","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.302"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2007.383035"},{"key":"ref60","first-page":"2172","article-title":"InfoGAN: Interpretable representation learning by information maximizing generative adversarial nets","author":"chen","year":"2016","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref62","first-page":"226","article-title":"Deep generative stochastic networks trainable by backprop","author":"bengio","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1162\/NECO_a_00158"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"key":"ref64","article-title":"LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop","author":"yu","year":"2015","journal-title":"arXiv 1506 03365"},{"key":"ref27","first-page":"448","article-title":"Deep Boltzmann machines","author":"salakhutdinov","year":"2009","journal-title":"Proc Int Conf Artif Intell Statist"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref66","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009"},{"key":"ref29","article-title":"Deep directed generative models with energy-based probability estimation","author":"kim","year":"2016","journal-title":"arXiv 1606 03439"},{"key":"ref67","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 Int Conf Neural Inf Process"},{"key":"ref68","article-title":"Interpolation inpainting","author":"d'errico","year":"2004"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1023\/A:1021669406132"},{"key":"ref2","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref20","first-page":"1486","article-title":"Deep generative image models using a laplacian pyramid of adversarial networks","author":"denton","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1201\/b10905-6"},{"key":"ref21","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref24","first-page":"847","article-title":"GRADE: Gibbs reaction and diffusion equations","author":"zhu","year":"1998","journal-title":"Proc IEEE Int Conf Comput Vis"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2010.00765.x"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1977.tb01600.x"},{"key":"ref25","first-page":"1976","article-title":"Alternating back-propagation for generator network","author":"han","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref50","author":"cover","year":"2012","journal-title":"Elements of Information Theory"},{"key":"ref51","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"arXiv 1502 03167"},{"key":"ref59","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"2017","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref58","article-title":"Energy-based generative adversarial network","author":"zhao","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref57","first-page":"2226","article-title":"Improved techniques for training GANs","author":"salimans","year":"2016","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref56","first-page":"487","article-title":"Learning deep features for scene recognition using places database","author":"zhou","year":"2014","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2003.819861"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref53","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref10","first-page":"1235","article-title":"Energy-based models for sparse overcomplete representations","volume":"4","author":"teh","year":"2003","journal-title":"J Mach Learn Res"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1090\/S0033-569X-07-01063-2"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00900"},{"key":"ref12","first-page":"1105","article-title":"Learning deep energy models","author":"ngiam","year":"2011","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref13","article-title":"Generative modeling of convolutional neural networks","author":"dai","year":"2015","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref14","first-page":"1902","article-title":"Learning FRAME models using CNN filters","author":"lu","year":"2016","journal-title":"Proc 30th AAAI Conf Artif Intell"},{"key":"ref15","first-page":"2635","article-title":"A theory of generative ConvNet","author":"xie","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref16","article-title":"Introspective generative modeling: Decide discriminatively","author":"lazarow","year":"2017","journal-title":"arXiv 1704 07820"},{"key":"ref17","first-page":"818","article-title":"Visualizing and understanding convolutional neural networks","author":"zeiler","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298761"},{"key":"ref19","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1162\/089976602760128018"},{"key":"ref3","article-title":"A tutorial on energy-based learning","author":"lecun","year":"2006","journal-title":"Predicting Structured Data"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.160"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1627"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2003.1201820"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/BF02293851"},{"key":"ref49","author":"grenander","year":"2007","journal-title":"Pattern Theory From Representation to Inference"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1023\/A:1023023207396"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015430"},{"key":"ref45","first-page":"1433","article-title":"Maximum entropy inverse reinforcement learning","author":"ziebart","year":"2008","journal-title":"Proc 23rd AAAI Conf Artif Intell"},{"key":"ref48","first-page":"654","article-title":"Learning continuous attractors in recurrent networks","author":"seung","year":"1998","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.79.8.2554"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.119"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390290"},{"key":"ref43","first-page":"177","article-title":"On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates","volume":"65","author":"younes","year":"1999","journal-title":"Stochastics An International Journal of Probability and Stochastic Processes"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/34\/8922815\/8519332-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/8922815\/08519332.pdf?arnumber=8519332","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T13:59:29Z","timestamp":1651067969000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8519332\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,1]]},"references-count":74,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2018.2879081","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,1]]}}}