{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:07:28Z","timestamp":1730246848223,"version":"3.28.0"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,9,1]],"date-time":"2019-09-01T00:00:00Z","timestamp":1567296000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,9]]},"DOI":"10.1109\/icip.2019.8803684","type":"proceedings-article","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T19:32:48Z","timestamp":1566847968000},"page":"4180-4184","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Hardware Friendly Bottleneck Architecture in CNN for Embedded Computing Systems"],"prefix":"10.1109","author":[{"given":"Xing","family":"Lei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longjun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiheng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongbin","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nanning","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref31"},{"key":"ref30","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","author":"glorot","year":"2010","journal-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics"},{"key":"ref10","article-title":"Squeeze-and-excitation networks","author":"hu","year":"2017","journal-title":"arXiv preprint arXiv 1709 04396"},{"key":"ref11","article-title":"Learning transferable architectures for scalable image recognition","author":"zoph","year":"2017","journal-title":"arXiv preprint arXiv 1707 07012"},{"key":"ref12","article-title":"Progressive neural architecture search","author":"liu","year":"2017","journal-title":"arXiv preprint arXiv 1712 00559"},{"key":"ref13","article-title":"Regularized evolution for image classifier architecture search","author":"real","year":"2018","journal-title":"arXiv preprint arxiv 1802 05807"},{"key":"ref14","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"han","year":"2016","journal-title":"International Conference on Learning Representations (ICLR)"},{"key":"ref15","article-title":"Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation","author":"sandler","year":"2018","journal-title":"arXiv preprint arXiv 1801 04381"},{"key":"ref16","article-title":"SqueezeNet: AlexNetlevel accuracy with 50x fewer parameters and <0.5MB model size","author":"iandola","year":"2016","journal-title":"arXiv preprint arXiv 1602 07360"},{"key":"ref17","article-title":"Xception: Deep learning with depthwise separable convolutions","author":"chollet","year":"2016","journal-title":"ArXiv Preprint"},{"key":"ref18","article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017","journal-title":"arXiv preprint arXiv 1704 04861"},{"key":"ref19","article-title":"Shufflenet: An extremely efficient convolutional neural network for mobile devices","author":"zhang","year":"2017","journal-title":"arXiv preprint arXiv 1707 01083"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-5110-1_9"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1989.1.4.541"},{"key":"ref3","article-title":"Very deep convolutional networks for large-scale image recognition[C]","author":"simonyan","year":"2015","journal-title":"International Conference on Learning Representations"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref29","first-page":"543","article-title":"A method for solving the convex programming problem with convergence rate o (1\/k&#x02C6;2)","volume":"269","author":"nesterov","year":"1983","journal-title":"Dokl Akad Nauk SSSR"},{"article-title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","year":"2016","author":"szegedy","key":"ref5"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref7","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"European Conference on Computer Vision"},{"key":"ref2","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"NIPS"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref1","first-page":"25433","article-title":"Deep learning an overview","volume":"10","author":"ramachandran","year":"2015","journal-title":"IJAER"},{"article-title":"DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices","year":"2017","author":"li","key":"ref20"},{"key":"ref22","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-01264-9_8","article-title":"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design","author":"ma","year":"2018"},{"key":"ref21","article-title":"Condensenet: An efficient densenet using learned group convolutions","author":"huang","year":"2017","journal-title":"arXiv preprint arXiv 1711 03890"},{"key":"ref24","article-title":"Interleaved group convolutions for deep neural networks","author":"zhang","year":"2017","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2018.00215"},{"key":"ref26","article-title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems","author":"abadi","year":"2016","journal-title":"arXiv preprint arXiv 1603 04467"},{"key":"ref25","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"ioffe","year":"2015","journal-title":"ICML"}],"event":{"name":"2019 IEEE International Conference on Image Processing (ICIP)","start":{"date-parts":[[2019,9,22]]},"location":"Taipei, Taiwan","end":{"date-parts":[[2019,9,25]]}},"container-title":["2019 IEEE International Conference on Image Processing (ICIP)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8791230\/8799366\/08803684.pdf?arnumber=8803684","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T14:43:52Z","timestamp":1658155432000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8803684\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/icip.2019.8803684","relation":{},"subject":[],"published":{"date-parts":[[2019,9]]}}}