{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:37:57Z","timestamp":1730302677603,"version":"3.28.0"},"reference-count":13,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1109\/vcip.2018.8698701","type":"proceedings-article","created":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T02:47:33Z","timestamp":1556246853000},"page":"1-4","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Weighted Kernel Sharing Convolutional Neural Networks"],"prefix":"10.1109","author":[{"given":"Helong","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Yie-Tarng","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Wen-Hsien","family":"Fang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009","journal-title":"Technical Report"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref13","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"NIPS"},{"key":"ref4","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2014","journal-title":"Deep Learning and Representation Learning Workshop NIPS"},{"key":"ref3","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"han","year":"2016","journal-title":"ICLRE"},{"key":"ref6","first-page":"2217","article-title":"Understanding and improving convolutional neural networks via concatenated rectified linear units","author":"shang","year":"2016","journal-title":"ICML"},{"article-title":"Squeezenet: Alexnet-level accuracy with 50× fewer parameters and¡ 0.5 mb model size","year":"2016","author":"iandola","key":"ref5"},{"article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","year":"2017","author":"howard","key":"ref8"},{"key":"ref7","article-title":"Interleaved group convolutions for deep neural networks","author":"zhang","year":"2017","journal-title":"ICCV"},{"key":"ref2","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"NIPS"},{"key":"ref1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"ICLRE"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"rumelhart","year":"1986","journal-title":"Nature"}],"event":{"name":"2018 IEEE Visual Communications and Image Processing (VCIP)","start":{"date-parts":[[2018,12,9]]},"location":"Taichung, Taiwan","end":{"date-parts":[[2018,12,12]]}},"container-title":["2018 IEEE Visual Communications and Image Processing (VCIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8694905\/8698608\/08698701.pdf?arnumber=8698701","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,13]],"date-time":"2019-05-13T23:00:45Z","timestamp":1557788445000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8698701\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12]]},"references-count":13,"URL":"https:\/\/doi.org\/10.1109\/vcip.2018.8698701","relation":{},"subject":[],"published":{"date-parts":[[2018,12]]}}}