{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:18:44Z","timestamp":1740169124973,"version":"3.37.3"},"reference-count":32,"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:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Basic Research Program of China","doi-asserted-by":"publisher","award":["2017YFB1400800"],"award-info":[{"award-number":["2017YFB1400800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61702046"],"award-info":[{"award-number":["61702046"]}],"id":[{"id":"10.13039\/501100001809","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.2019.2932773","type":"journal-article","created":{"date-parts":[[2019,8,2]],"date-time":"2019-08-02T15:56:12Z","timestamp":1564761372000},"page":"105470-105478","source":"Crossref","is-referenced-by-count":2,"title":["CNN Compression-Recovery Framework via Rank Allocation Decomposition With Knowledge Transfer"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9987-3175","authenticated-orcid":false,"given":"Zhonghong","family":"Ou","sequence":"first","affiliation":[]},{"given":"Yunfeng","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Huihui","family":"Kong","sequence":"additional","affiliation":[]},{"given":"Meina","family":"Song","sequence":"additional","affiliation":[]},{"given":"Yingxia","family":"Shao","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref32","first-page":"2285","article-title":"Compressing neural networks with the hashing trick","author":"chen","year":"2015","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref31","first-page":"1","article-title":"Model compression via distillation and quantization","author":"polino","year":"2018","journal-title":"Proc ICLR"},{"key":"ref30","first-page":"1","article-title":"Compression of deep convolutional neural networks for fast and low power mobile applications","author":"kim","year":"2016","journal-title":"Proc ICLR"},{"key":"ref10","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/330"},{"key":"ref12","first-page":"1424","article-title":"Espace: Accelerating convolutional neural networks via eliminating spatial and channel redundancy","author":"lin","year":"2017","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2873305"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/336"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"ref17","first-page":"525","article-title":"XNOR-Net: Imagenet classification using binary convolutional neural networks","author":"rastegari","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref18","first-page":"1","article-title":"Fitnets: Hints for thin deep nets","author":"romero","year":"2015","journal-title":"Proc Int Conf Learn Represent"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref28","first-page":"1753","article-title":"Towards convolutional neural networks compression via global error reconstruction","author":"lin","year":"2016","journal-title":"Proc 35th Int Joint Conf Artif Intell"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2502579"},{"key":"ref3","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"han","year":"2015","journal-title":"arXiv 1510 00149 [cs]"},{"key":"ref6","article-title":"Like what you like: Knowledge distill via neuron selectivity transfer","author":"huang","year":"2017","journal-title":"arXiv 1707 01219"},{"key":"ref29","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":"ref5","first-page":"1","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015","journal-title":"Deep Learning and Representation Learning Workshop NIPS"},{"key":"ref8","first-page":"1","article-title":"Speeding up convolutional neural networks with low rank expansions","author":"jaderberg","year":"2014","journal-title":"Proc BMVC"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref2","first-page":"1269","article-title":"Exploiting linear structure within convolutional networks for efficient evaluation","author":"denton","year":"2014","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"ref1","first-page":"742","article-title":"Learning efficient object detection models with knowledge distillation","author":"chen","year":"2017","journal-title":"Proc Adv Neural Inf Process Syst"},{"key":"ref20","first-page":"1","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc ICLR"},{"key":"ref22","first-page":"1","article-title":"Convolutional neural networks with low-rank regularization","author":"tai","year":"2016","journal-title":"Proc ICLR"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01237-3_17"},{"article-title":"The caltech-ucsd birds-200-2011 dataset","year":"2011","author":"wah","key":"ref23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.15"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2895330"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08786129.pdf?arnumber=8786129","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:33:00Z","timestamp":1641987180000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8786129\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":32,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2932773","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2019]]}}}