{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T06:44:33Z","timestamp":1784529873320,"version":"3.55.0"},"reference-count":24,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2018]]},"DOI":"10.1109\/access.2018.2877890","type":"journal-article","created":{"date-parts":[[2018,10,24]],"date-time":"2018-10-24T18:59:21Z","timestamp":1540407561000},"page":"64270-64277","source":"Crossref","is-referenced-by-count":674,"title":["Benchmark Analysis of Representative Deep Neural Network Architectures"],"prefix":"10.1109","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7070-1545","authenticated-orcid":false,"given":"Simone","family":"Bianco","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Remi","family":"Cadene","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5925-2646","authenticated-orcid":false,"given":"Luigi","family":"Celona","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9112-0574","authenticated-orcid":false,"given":"Paolo","family":"Napoletano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref11","author":"iandola","year":"2016","journal-title":"SqueezeNet AlexNet-level accuracy with 50x fewer parameters and"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref14","first-page":"12","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"szegedy","year":"2016","journal-title":"Proc Workshop Int Conf Learn Represent"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref18","first-page":"4467","article-title":"Dual path networks","author":"chen","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref4","author":"canziani","year":"2016","journal-title":"An analysis of deep neural network models for practical applications"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref6","article-title":"Automatic differentiation in PyTorch","author":"paszke","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst (NIPS) Workshop"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.351"},{"key":"ref8","author":"simonyan","year":"2014","journal-title":"Very Deep Convolutional Networks for Large-scale Image Recognition"},{"key":"ref7","author":"bianco","year":"2018","journal-title":"Paper Github Repository"},{"key":"ref2","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst (NIPS)"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"lecun","year":"2015","journal-title":"Nature"},{"key":"ref9","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"Proc Int Conf Mach Learn (ICML)"},{"key":"ref20","first-page":"8697","article-title":"Learning transferable architectures for scalable image recognition","author":"zoph","year":"2018","journal-title":"Proc Conf Comput Vis Pattern Recognit (CVPR)"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref21","author":"howard","year":"2017","journal-title":"Mobilenets Efficient convolutional neural networks for mobile vision applications"},{"key":"ref24","author":"han","year":"2015","journal-title":"Deep compression Compressing deep neural networks with pruning trained quantization and huffman coding"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8274985\/08506339.pdf?arnumber=8506339","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T13:12:41Z","timestamp":1643289161000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8506339\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/access.2018.2877890","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]}}}