{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T14:13:46Z","timestamp":1774534426161,"version":"3.50.1"},"reference-count":35,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology (MOST) of the Republic of China","doi-asserted-by":"publisher","award":["111-2221-E-032-021-"],"award-info":[{"award-number":["111-2221-E-032-021-"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2022]]},"DOI":"10.1109\/access.2022.3196492","type":"journal-article","created":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T19:20:19Z","timestamp":1659640819000},"page":"82834-82843","source":"Crossref","is-referenced-by-count":12,"title":["ThreshNet: An Efficient DenseNet Using Threshold Mechanism to Reduce Connections"],"prefix":"10.1109","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2240-1377","authenticated-orcid":false,"given":"Rui-Yang","family":"Ju","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Tamkang University, New Taipei City, Tamsui, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3693-7121","authenticated-orcid":false,"given":"Ting-Yu","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Engineering Science, National Cheng Kung University, Tainan City, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1874-1002","authenticated-orcid":false,"given":"Jia-Hao","family":"Jian","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Tamkang University, New Taipei City, Tamsui, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7536-8967","authenticated-orcid":false,"given":"Jen-Shiun","family":"Chiang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Tamkang University, New Taipei City, Tamsui, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Tamkang University, New Taipei City, Tamsui, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref4","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"Howard","year":"2017","journal-title":"arXiv:1704.04861"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref6","first-page":"17","article-title":"ALBERT: A lite BERT for self-supervised learning of language representations","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Lan"},{"key":"ref7","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume-title":"Proc. Int. Conf. Mach. Learn.","volume":"37","author":"Ioffe"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref9","article-title":"SqueezeNet: AlexNet-level accuracy with 50\u00d7 fewer parameters and <0.5MB model size","author":"Iandola","year":"2016","journal-title":"arXiv:1602.07360"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref11","first-page":"6105","article-title":"EfficientNet: Rethinking model scaling for convolutional neural networks","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Tan"},{"key":"ref12","first-page":"10096","article-title":"EfficientNetv2: Smaller models and faster training","volume-title":"Proc. Int. Conf. Mach. Learn. (ICML)","author":"Tan"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/tnnls.2022.3166101"},{"key":"ref14","first-page":"14014","article-title":"Are sixteen heads really better than one?","volume-title":"Proc. Conf. Neural Inf. Process. Syst. (NeurIPS)","author":"Michel"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.01022"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-09276-9"},{"key":"ref18","article-title":"DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter","author":"Sanh","year":"2019","journal-title":"arXiv:1910.01108"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.findings-emnlp.372"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00365"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref23","article-title":"Bridging the gaps between residual learning, recurrent neural networks and visual cortex","author":"Liao","year":"2016","journal-title":"arXiv:1604.03640"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref25","first-page":"315","article-title":"Deep sparse rectifier neural networks","volume-title":"Proc. 14th Int. Conf. Artif. Intell. Statist.","author":"Glorot"},{"key":"ref26","article-title":"Log-DenseNet: How to sparsify a DenseNet","author":"Hu","year":"2017","journal-title":"arXiv:1711.00002"},{"key":"ref27","first-page":"11","article-title":"FractalNet: Ultra-deep neural networks without residuals","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Larsson"},{"key":"ref28","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.2118\/18761-MS"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.5244\/C.30.87"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.l007\/978-3-319-46448-0_2"},{"key":"ref33","article-title":"YOLOv4: Optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020","journal-title":"arXiv:2004.10934"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01513-4"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9668973\/09849638.pdf?arnumber=9849638","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T11:44:40Z","timestamp":1706787880000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9849638\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"references-count":35,"URL":"https:\/\/doi.org\/10.1109\/access.2022.3196492","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]}}}