{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:18:19Z","timestamp":1740169099397,"version":"3.37.3"},"reference-count":37,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61602383","61772424","61702418"],"award-info":[{"award-number":["61602383","61772424","61702418"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"publisher","award":["2017JQ6019","2018JQ6016"],"award-info":[{"award-number":["2017JQ6019","2018JQ6016"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2905138","type":"journal-article","created":{"date-parts":[[2019,3,14]],"date-time":"2019-03-14T18:44:38Z","timestamp":1552589078000},"page":"38264-38272","source":"Crossref","is-referenced-by-count":4,"title":["Sensitivity-Oriented Layer-Wise Acceleration and Compression for Convolutional Neural Network"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9715-6957","authenticated-orcid":false,"given":"Wei","family":"Zhou","sequence":"first","affiliation":[]},{"given":"Yue","family":"Niu","sequence":"additional","affiliation":[]},{"given":"Guanwen","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref33","first-page":"2148","article-title":"Predicting parameters in deep learning","author":"denil","year":"2013","journal-title":"Proc Adv Neural Inf Process Syst"},{"journal-title":"Overfeat Integrated Recognition Localization and Detection Using Convolutional Networks","year":"2013","author":"sermanet","key":"ref32"},{"key":"ref31","first-page":"947","article-title":"PerforatedCNNs: Acceleration through elimination of redundant convolutions","author":"figurnov","year":"2016","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCD.2013.6657019"},{"journal-title":"OverFeat Object Recognizer Feature Extractor","year":"2017","author":"pierre","key":"ref37"},{"journal-title":"TORCH A Scientific Computing Framework for Luajit","year":"2017","author":"collobert","key":"ref36"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"journal-title":"Large Scale Visual Recognition Challenge 2010","year":"2017","author":"alex","key":"ref34"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"clement","year":"2013","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2014.2360798"},{"key":"ref12","first-page":"541","article-title":"A novel approach for annotation-based image retrieval using deep architecture","volume":"30","author":"zhang","year":"2018","journal-title":"J Multiple-Valued Logic Soft Comput"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2766843"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2016.2598092"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2015.2477040"},{"journal-title":"Convolutional neural networks for sentence classification","year":"2014","author":"yoon","key":"ref16"},{"key":"ref17","first-page":"2094","article-title":"Deep reinforcement learning with double Q-learning","author":"van hasselt","year":"2016","journal-title":"Proc 30th AAAI Conf Artif Intell"},{"key":"ref18","first-page":"164","article-title":"Second order derivatives for network pruning: Optimal brain surgeon","author":"babak","year":"1993","journal-title":"Proc Adv Neural Inf Process Syst"},{"journal-title":"Deep compression Compressing deep neural networks with pruning trained quantization and huffman coding","year":"2015","author":"han","key":"ref19"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1145\/2847263.2847265"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2967280"},{"journal-title":"Very Deep Convolutional Networks for Large-scale Image Recognition","year":"2014","author":"karen","key":"ref3"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref29","first-page":"161","article-title":"Optimizing FPGA-based accelerator design for deep convolutional neural networks","author":"chen","year":"2015","journal-title":"Proc ACM\/SIGDA Int Symp Field-Program Gate Arrays"},{"key":"ref5","first-page":"1","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"christian","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref7","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref2","first-page":"1","article-title":"Imagenet classification with deep convolutional neural networks","author":"hinton","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref9","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"jonathan","year":"2015","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/5254.708428"},{"journal-title":"Incremental network quantization Towards lossless cnns with low-precision weights","year":"2017","author":"zhou","key":"ref20"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2014.2308415"},{"key":"ref21","first-page":"2285","article-title":"Compressing neural networks with the hashing trick","author":"wenlin","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"journal-title":"Speeding up convolutional neural networks with low rank expansions","year":"2014","author":"max","key":"ref24"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2016.2638624"},{"journal-title":"Speeding-up convolutional neural networks using fine-tuned cp-decomposition","year":"2014","author":"vadim","key":"ref26"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298809"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08667287.pdf?arnumber=8667287","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,10]],"date-time":"2021-08-10T19:40:29Z","timestamp":1628624429000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8667287\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":37,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2905138","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2019]]}}}