{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,6]],"date-time":"2025-06-06T20:50:38Z","timestamp":1749243038088,"version":"3.37.3"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2947567","type":"journal-article","created":{"date-parts":[[2019,10,15]],"date-time":"2019-10-15T22:08:17Z","timestamp":1571177297000},"page":"150175-150183","source":"Crossref","is-referenced-by-count":2,"title":["Generative Model With Dynamic Linear Flow"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3401-3032","authenticated-orcid":false,"given":"Huadong","family":"Liao","sequence":"first","affiliation":[]},{"given":"Jiawei","family":"He","sequence":"additional","affiliation":[]},{"given":"Kunxian","family":"Shu","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref31","article-title":"Image transformer","author":"parmar","year":"2018","journal-title":"arXiv 1802 05751"},{"key":"ref30","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref10","article-title":"Auto-encoding variational Bayes","author":"kingma","year":"2013","journal-title":"arXiv 1312 6114"},{"key":"ref11","article-title":"Pixel recurrent neural networks","author":"van den oord","year":"2016","journal-title":"arXiv 1601 06759"},{"key":"ref12","article-title":"PixelCNN: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications","author":"salimans","year":"2017","journal-title":"arXiv 1701 05517"},{"key":"ref13","article-title":"PixelSNAIL: An improved autoregressive generative model","author":"chen","year":"2017","journal-title":"arXiv 1712 09763"},{"key":"ref14","article-title":"Generating high fidelity images with subscale pixel networks and multidimensional upscaling","author":"menick","year":"2018","journal-title":"arXiv 1812 01608"},{"key":"ref15","article-title":"NICE: Non-linear independent components estimation","author":"dinh","year":"2014","journal-title":"arXiv 1410 8516"},{"key":"ref16","article-title":"Density estimation using real NVP","author":"dinh","year":"2016","journal-title":"arXiv 1605 08803"},{"key":"ref17","first-page":"10215","article-title":"Glow: Generative flow with invertible \n$1\\times1$\n convolutions","author":"kingma","year":"2018","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref18","article-title":"Parallel WaveNet: Fast high-fidelity speech synthesis","author":"van den oord","year":"2017","journal-title":"arXiv 1711 10433"},{"key":"ref19","first-page":"2338","article-title":"Masked autoregressive flow for density estimation","author":"papamakarios","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref4","first-page":"5998","article-title":"Attention is all you need","author":"vaswani","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref6","first-page":"87","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"radford","year":"2019","journal-title":"OpenAIRE blog"},{"key":"ref29","article-title":"Progressive growing of GANs for improved quality, stability, and variation","author":"karras","year":"2017","journal-title":"arXiv 1710 10196"},{"key":"ref5","article-title":"BERT: Pre-training of deep bidirectional transformers for language understanding","author":"devlin","year":"2018","journal-title":"arXiv 1810 04805"},{"key":"ref8","first-page":"854","article-title":"Parseval networks: Improving robustness to adversarial examples","volume":"70","author":"cisse","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"},{"key":"ref7","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","author":"kingma","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref9","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref1","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref20","first-page":"4743","article-title":"Improved variational inference with inverse autoregressive flow","author":"kingma","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref22","article-title":"Flow: Improving flow-based generative models with variational dequantization and architecture design","author":"ho","year":"2019","journal-title":"arXiv 1902 00275"},{"key":"ref21","article-title":"WaveNet: A generative model for raw audio","author":"van den oord","year":"2016","journal-title":"arXiv 1609 03499"},{"key":"ref24","first-page":"4797","article-title":"Conditional image generation with PixelCNN decoders","author":"van den oord","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref23","article-title":"TzK flow&#x2014;Conditional generative model","author":"livne","year":"2018","journal-title":"arXiv 1811 01837"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref26"},{"key":"ref25","first-page":"2912","article-title":"Parallel multiscale autoregressive density estimation","volume":"70","author":"reed","year":"2017","journal-title":"Proc 34th Int Conf Mach Learn"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08869769.pdf?arnumber=8869769","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T19:40:56Z","timestamp":1628624456000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8869769\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2947567","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2019]]}}}