{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T06:37:38Z","timestamp":1778395058648,"version":"3.51.4"},"reference-count":42,"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\/100000104","name":"Space Technology Research Institute through the National Aeronautics and Space Administration\u2019s (NASA\u2019s) Space Technology Research Grants Program","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000104","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2020.3047764","type":"journal-article","created":{"date-parts":[[2020,12,28]],"date-time":"2020-12-28T20:46:56Z","timestamp":1609188416000},"page":"3936-3946","source":"Crossref","is-referenced-by-count":22,"title":["Tackling Small Data Challenges in Visual Fire Detection: A Deep Convolutional Generative Adversarial Network Approach"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8498-3483","authenticated-orcid":false,"given":"Zhaoyi","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1115-7383","authenticated-orcid":false,"given":"Yanjie","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7590-9399","authenticated-orcid":false,"given":"Joseph Homer","family":"Saleh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","first-page":"3","article-title":"Rectifier nonlinearities improve neural network acoustic models","volume":"30","author":"maas","year":"2013","journal-title":"Proc ICML"},{"key":"ref38","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"2014","journal-title":"arXiv 1412 6980"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.04.083"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref31","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"arXiv 1409 1556"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-65172-9_16"},{"key":"ref37","article-title":"Optimizing the latent space of generative networks","author":"bojanowski","year":"2017","journal-title":"arXiv 1707 05776"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2019.2897594"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2812835"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2018.2830099"},{"key":"ref10","author":"forsyth","year":"2002","journal-title":"Computer Vision A Modern Approach"},{"key":"ref40","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < 0.5 MB model size","author":"iandola","year":"2016","journal-title":"arXiv 1602 07360"},{"key":"ref11","author":"szeliski","year":"2010","journal-title":"Computer Vision Algorithms and Applications"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/7068349"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCAR.2018.8384711"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/s10694-017-0695-6"},{"key":"ref15","article-title":"FireNet: A specialized lightweight fire & smoke detection model for real-time IoT applications","author":"jadon","year":"2019","journal-title":"arXiv 1905 11922"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref19","first-page":"1","article-title":"Data quality considerations for big data and machine learning: Going beyond data cleaning and transformations","volume":"10","author":"gudivada","year":"2017","journal-title":"Int J Adv Software"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ECTICon.2012.6254144"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3003848"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCRD.2011.5764295"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2934163"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2991338"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.2991\/ifmeita-16.2016.105"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3010212"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2953558"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2902606"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2990224"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1177\/1042391503013002003"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2982994"},{"key":"ref20","first-page":"1","article-title":"Data infrastructure for machine learning","author":"breck","year":"2018","journal-title":"Proc SysML Conf"},{"key":"ref22","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"radford","year":"2015","journal-title":"arXiv 1511 06434"},{"key":"ref21","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1561\/9781680836233"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00206"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1093\/nsr\/nwx106"},{"key":"ref23","article-title":"Large scale GAN training for high fidelity natural image synthesis","author":"brock","year":"2018","journal-title":"arXiv 1809 11096"},{"key":"ref26","first-page":"1794","article-title":"Fire and smoke detection without sensors: Image processing based approach","author":"\u00e7elik","year":"2007","journal-title":"Proc 15th Eur Signal Process Conf"},{"key":"ref25","first-page":"1707","article-title":"An early fire-detection method based on image processing","volume":"4","author":"chen","year":"2004","journal-title":"Proc Int Conf Image Process (ICIP)"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09309261.pdf?arnumber=9309261","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,17]],"date-time":"2021-12-17T19:55:49Z","timestamp":1639770949000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9309261\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":42,"URL":"https:\/\/doi.org\/10.1109\/access.2020.3047764","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}