{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T05:05:31Z","timestamp":1749531931351,"version":"3.37.3"},"reference-count":46,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008788","name":"National Institute of Technology Karnataka (NITK), Surathkal, India","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008788","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2024]]},"DOI":"10.1109\/access.2024.3416997","type":"journal-article","created":{"date-parts":[[2024,6,20]],"date-time":"2024-06-20T17:42:14Z","timestamp":1718905334000},"page":"94914-94925","source":"Crossref","is-referenced-by-count":3,"title":["Channel Pruning of Transfer Learning Models Using Novel Techniques"],"prefix":"10.1109","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7702-8130","authenticated-orcid":false,"given":"Pragnesh","family":"Thaker","sequence":"first","affiliation":[{"name":"National Institute of Technology Karnataka, Surathkal, Srinivasnagar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biju R.","family":"Mohan","sequence":"additional","affiliation":[{"name":"National Institute of Technology Karnataka, Surathkal, Srinivasnagar, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jvcir.2019.102654"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3092571"},{"key":"ref3","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.120143","article-title":"Stereoscopic image super-resolution with interactive memory learning","volume":"227","author":"Zhu","year":"2023","journal-title":"Exp. Syst. Appl."},{"key":"ref4","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"Hinton","year":"2012","journal-title":"arXiv:1207.0580"},{"key":"ref5","first-page":"1097","article-title":"ImageNet classification with deep convolutional neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NIPS)","author":"Krizhevsky"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref7","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"article-title":"Deep compression: Compressing deep neural network with pruning, trained quantization and Huffman coding","volume-title":"Proc. 4th Int. Conf. Learn. Represent.","author":"Han","key":"ref9"},{"issue":"3","key":"ref10","doi-asserted-by":"crossref","first-page":"60","DOI":"10.3390\/computers12030060","article-title":"Model compression for deep neural networks: A survey","volume":"12","author":"Li","year":"2023","journal-title":"Computers"},{"key":"ref11","first-page":"2270","article-title":"Learning the number of neurons in deep networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Alvarez"},{"key":"ref12","article-title":"When to prune? A policy towards early structural pruning","author":"Shen","year":"2021","journal-title":"arXiv:2110.12007"},{"key":"ref13","first-page":"1135","article-title":"Learning both weights and connections for efficient neural network","volume-title":"Proc. NIPS","author":"Han"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2021.3078436"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01197"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102417"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3005348"},{"key":"ref19","first-page":"24604","article-title":"CHIP: Channel independence-based pruning for compact neural networks","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"34","author":"Sui"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/I2CT54291.2022.9825302"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00290"},{"key":"ref22","article-title":"Pruning filters for efficient ConvNets","author":"Li","year":"2016","journal-title":"arXiv:1608.08710"},{"key":"ref23","first-page":"1","article-title":"Synaptic strength for convolutional neural network","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"31","author":"Lin"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/336"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00291"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref28","article-title":"Network in network","author":"Lin","year":"2013","journal-title":"arXiv:1312.4400"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00447"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2019.2906563"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01467"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.neunet.2022.05.002","article-title":"LAP: Latency-aware automated pruning with dynamic-based filter selection","volume":"152","author":"Chen","year":"2022","journal-title":"Neural Netw."},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539260"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.3390\/app122111184"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/309"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00083"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00508"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00339"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/MIPR49039.2020.00037"},{"key":"ref40","first-page":"1","article-title":"Pruning convolutional neural networks for resource efficient inference","volume-title":"Proc. 5th Int. Conf. Learn. Represent.","author":"Molchanov"},{"issue":"7","key":"ref41","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.3390\/sym14071372","article-title":"Channel pruning base on joint reconstruction error for neural network","volume":"14","author":"Li","year":"2022","journal-title":"Symmetry"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.5244\/C.28.88"},{"key":"ref43","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2017","journal-title":"arXiv:1412.6980"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2017.58"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ab81e2"},{"volume-title":"CIFAR-10 and CIFAR-100 Datasets\u2014Cs.toronto.edu","year":"2024","key":"ref46"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10380310\/10565832.pdf?arnumber=10565832","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T05:54:41Z","timestamp":1721368481000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10565832\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"references-count":46,"URL":"https:\/\/doi.org\/10.1109\/access.2024.3416997","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2024]]}}}