{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,7]],"date-time":"2024-09-07T16:07:22Z","timestamp":1725725242730},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,7,1]],"date-time":"2020-07-01T00:00:00Z","timestamp":1593561600000},"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":[[2020,7]]},"DOI":"10.1109\/icmew46912.2020.9105960","type":"proceedings-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T17:39:52Z","timestamp":1591724392000},"page":"1-6","source":"Crossref","is-referenced-by-count":2,"title":["A Highly Efficient Training-Aware Convolutional Neural Network Compression Paradigm"],"prefix":"10.1109","author":[{"given":"Chang","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongtao","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","year":"2015","author":"han","key":"ref10"},{"journal-title":"8-bit approximations for parallelism in deep learning","year":"2015","author":"dettmers","key":"ref11"},{"key":"ref12","first-page":"3123","article-title":"Binaryconnect: Training deep neural networks with binary weights during propagations","author":"courbariaux","year":"2015","journal-title":"Advances in neural information processing systems"},{"article-title":"Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1","year":"2016","author":"courbariaux","key":"ref13"},{"key":"ref14","first-page":"345","article-title":"Towards accurate binary convolutional neural network","author":"lin","year":"2017","journal-title":"Advances in neural information processing systems"},{"article-title":"Trained ternary quantization","year":"2016","author":"zhu","key":"ref15"},{"article-title":"Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients","year":"2016","author":"zhou","key":"ref16"},{"article-title":"Wrpn: wide reduced-precision networks","year":"2017","author":"mishra","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01267-0_44"},{"key":"ref19","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v31i1.10862","article-title":"How to train a compact binary neural network with high accuracy?","author":"tang","year":"2017","journal-title":"Thirty-First AAAI Conference on Artificial Intelligence"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref6","first-page":"1269","article-title":"Exploiting linear structure within convolutional networks for efficient evaluation","author":"denton","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref5","first-page":"2830","article-title":"Data-free parameter pruning for deep neural networks","author":"srinivas","year":"2015","journal-title":"Computer Science"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"article-title":"Mobilenets: Efficient convolutional neural networks for mobile vision applications","year":"2017","author":"howard","key":"ref7"},{"article-title":"Very deep convolutional networks for large-scale image recognition","year":"2014","author":"simonyan","key":"ref2"},{"key":"ref1","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"Advances in neural information processing systems"},{"key":"ref9","first-page":"2285","article-title":"Compressing neural networks with the hashing trick","author":"chen","year":"2015","journal-title":"International Conference on Machine Learning"},{"article-title":"Bnn+: Improved binary network training","year":"2018","author":"darabi","key":"ref20"},{"article-title":"mixup: Beyond empirical risk minimization","year":"2017","author":"dauphin","key":"ref22"},{"key":"ref21","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009","journal-title":"Citeseer Tech Rep"},{"article-title":"Improved regularization of convolutional neural networks with cutout","year":"2017","author":"devries","key":"ref23"}],"event":{"name":"2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)","start":{"date-parts":[[2020,7,6]]},"location":"London, United Kingdom","end":{"date-parts":[[2020,7,10]]}},"container-title":["2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9099356\/9105943\/09105960.pdf?arnumber=9105960","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,27]],"date-time":"2022-10-27T08:56:32Z","timestamp":1666860992000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9105960\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/icmew46912.2020.9105960","relation":{},"subject":[],"published":{"date-parts":[[2020,7]]}}}