{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:26:53Z","timestamp":1774538813352,"version":"3.50.1"},"reference-count":70,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,3,1]],"date-time":"2020-03-01T00:00:00Z","timestamp":1583020800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2020,3,1]]},"DOI":"10.1109\/tpami.2018.2886192","type":"journal-article","created":{"date-parts":[[2018,12,12]],"date-time":"2018-12-12T20:45:00Z","timestamp":1544647500000},"page":"568-579","source":"Crossref","is-referenced-by-count":110,"title":["Deep Neural Network Compression by In-Parallel Pruning-Quantization"],"prefix":"10.1109","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2895-006X","authenticated-orcid":false,"given":"Frederick","family":"Tung","sequence":"first","affiliation":[]},{"given":"Greg","family":"Mori","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref70","first-page":"740","article-title":"Microsoft COCO: Common objects in context","author":"lin","year":"2014","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref39","first-page":"2245","article-title":"Sparse deep transfer learning for convolutional neural network","author":"liu","year":"2017","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref38","article-title":"DSD: Dense-sparse-dense training for deep neural networks","author":"han","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.280"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.5244\/C.29.31"},{"key":"ref31","first-page":"598","article-title":"Optimal brain damage","author":"lecun","year":"1990","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref30","first-page":"164","article-title":"Second order derivatives for network pruning: Optimal brain surgeon","author":"hassibi","year":"1992","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"ref36","article-title":"Faster CNNs with direct sparse convolutions and guided pruning","author":"park","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.541"},{"key":"ref34","first-page":"2082","article-title":"Learning structured sparsity in deep neural networks","author":"wen","year":"2016","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref60","first-page":"1778","article-title":"Bayesian optimization in high dimensions via random embeddings","author":"wang","year":"2013","journal-title":"Proc Int Joint Conf Artif Intell"},{"key":"ref62","first-page":"ii-937","article-title":"Bayesian optimization with inequality constraints","author":"gardner","year":"2014","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref61","author":"rasmussen","year":"2006","journal-title":"Gaussian Processes for Machine Learning"},{"key":"ref63","first-page":"2951","article-title":"Practical Bayesian optimization of machine learning algorithms","author":"snoek","year":"2012","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref28","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017"},{"key":"ref64","article-title":"ImageNet Large Scale Visual Recognition Challenge","author":"russakovsky","year":"2014"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00821"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_40"},{"key":"ref66","article-title":"To prune, or not to prune: Exploring the efficacy of pruning for model compression","author":"zhu","year":"2018","journal-title":"Proc Workshop Int Conf Learn Represent"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2808319"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.279"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.540"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref1","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.205"},{"key":"ref22","first-page":"947","article-title":"PerforatedCNNs: Acceleration through elimination of redundant convolutions","author":"figurnov","year":"2016","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.194"},{"key":"ref24","article-title":"Multi-level residual networks from dynamical systems view","author":"chang","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.684"},{"key":"ref26","first-page":"2148","article-title":"Predicting parameters in deep learning","author":"denil","year":"2013","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0_39"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.173"},{"key":"ref51","first-page":"1269","article-title":"Exploiting linear structure within convolutional networks for efficient evaluation","author":"denton","year":"2014","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref59","article-title":"Multi-scale dense networks for resource efficient image classification","author":"huang","year":"2018","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref57","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and $<$<0.5 mb model size","author":"iandola","year":"2016"},{"key":"ref56","article-title":"N2N learning: Network to network compression via policy gradient reinforcement learning","author":"ashok","year":"2018","journal-title":"Proc Int Conf Learning Representations"},{"key":"ref55","article-title":"FitNets: Hints for thin deep nets","author":"romero","year":"2015","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref54","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298809"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.5244\/C.28.88"},{"key":"ref10","article-title":"Multi-scale context aggregation by dilated convolutions","author":"yu","year":"2016","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.93"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.643"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.9"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.11"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46478-7_28"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref16","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref17","first-page":"1379","article-title":"Dynamic network surgery for efficient DNNs","author":"guo","year":"2016","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref18","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding","author":"han","year":"2016","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.5244\/C.31.115"},{"key":"ref4","first-page":"379","article-title":"R-FCN: Object detection via region-based fully convolutional networks","author":"dai","year":"2016","journal-title":"Proc Advances Neural Inf Process Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref5","first-page":"21","article-title":"SSD: Single shot multibox detector","author":"liu","year":"2016","journal-title":"Proc Eur Conf Comput Vis"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref7","article-title":"Semantic image segmentation with deep convolutional nets and fully connected CRFs","author":"chen","year":"2015","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.327"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.353"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.430"},{"key":"ref45","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":"ref48","doi-asserted-by":"publisher","DOI":"10.1016\/0031-3203(95)00067-4"},{"key":"ref47","first-page":"4284","article-title":"Local binary convolutional neural networks","author":"xu","year":"2017","journal-title":"Proc IEEE Conf Comput Vis Pattern Recognit"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.761"},{"key":"ref41","article-title":"Soft weight-sharing for neural network compression","author":"ullrich","year":"2017","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref44","first-page":"3123","article-title":"BinaryConnect: Training deep neural networks with binary weights during propagations","author":"courbariaux","year":"2015","journal-title":"Proc Adv Neural Process Syst"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.521"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/8984605\/08573867.pdf?arnumber=8573867","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,27]],"date-time":"2022-04-27T13:59:47Z","timestamp":1651067987000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8573867\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,1]]},"references-count":70,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2018.2886192","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,1]]}}}