{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:16:54Z","timestamp":1780636614935,"version":"3.54.1"},"reference-count":49,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"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":[[2019,7]]},"DOI":"10.1109\/ijcnn.2019.8852463","type":"proceedings-article","created":{"date-parts":[[2019,10,1]],"date-time":"2019-10-01T03:44:32Z","timestamp":1569901472000},"page":"1-8","source":"Crossref","is-referenced-by-count":25,"title":["Structured Pruning for Efficient ConvNets via Incremental Regularization"],"prefix":"10.1109","author":[{"given":"Huan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiming","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuehai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoji","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Distilling the knowledge in a neural network","author":"hinton","year":"2015"},{"key":"ref38","article-title":"Do deep nets really need to be deep?","author":"ba","year":"2014","journal-title":"NeurIPS"},{"key":"ref33","article-title":"XNOR-net: Imagenet classification using binary convolutional neural networks","author":"rastegari","year":"2016","journal-title":"ECCV"},{"key":"ref32","article-title":"Neural networks with few multiplications","author":"lin","year":"2016"},{"key":"ref31","article-title":"BinaryNet: Training deep neural networks with weights and activations constrained to +1 or ?1","author":"courbariaux","year":"2016"},{"key":"ref30","article-title":"Compressing neural networks with the hashing trick","author":"chen","year":"2015","journal-title":"ICML"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/1150402.1150464"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2502579"},{"key":"ref35","article-title":"Speeding-up convolutional neural networks using fine-tuned CP-decomposition","author":"lebedev","year":"2016"},{"key":"ref34","article-title":"Exploiting linear structure within convolutional networks for efficient evaluation","author":"denton","year":"2014","journal-title":"NeurIPS"},{"key":"ref28","article-title":"ShuffleNet: An extremely efficient convolutional neural network for mobile devices","author":"zhang","year":"2017"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_8"},{"key":"ref2","article-title":"Compact deep convolutional neural networks with coarse pruning","author":"anwar","year":"2016"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001163"},{"key":"ref20","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from over-fitting","volume":"15","author":"srivastava","year":"2014","journal-title":"JMLR"},{"key":"ref22","article-title":"Bayesian compression for deep learning","author":"louizos","year":"2017","journal-title":"NeurIPS"},{"key":"ref21","article-title":"Structured probabilistic pruning for convolutional neural network acceleration","author":"wang","year":"2018","journal-title":"BMVC"},{"key":"ref24","article-title":"Structured bayesian pruning via log-normal multiplicative noise","author":"neklyudov","year":"2017","journal-title":"NeurIPS"},{"key":"ref23","article-title":"Variational dropout sparsifies deep neural networks","author":"molchanov","year":"2017","journal-title":"ICML"},{"key":"ref26","article-title":"MobileNets: Efficient convolutional neural networks for mobile vision applications","author":"howard","year":"2017"},{"key":"ref25","article-title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","author":"iandola","year":"2016"},{"key":"ref10","article-title":"How transferable are features in deep neural networks?","author":"yosinski","year":"2014","journal-title":"NeurIPS"},{"key":"ref11","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.12262","article-title":"Auto-balanced filter pruning for efficient convolutional neural networks","author":"ding","year":"2018","journal-title":"AAAI"},{"key":"ref40","article-title":"Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer","author":"zagoruyko","year":"2017","journal-title":"ICLRE"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/72.248452"},{"key":"ref14","article-title":"Deep Compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"han","year":"2016","journal-title":"ICLRE"},{"key":"ref15","article-title":"Learning both weights and connections for efficient neural network","author":"han","year":"2015","journal-title":"NeurIPS"},{"key":"ref16","article-title":"Pruning filters for efficient convnets","author":"li","year":"2017","journal-title":"ICLRE"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-9868.2005.00532.x"},{"key":"ref19","article-title":"Improving neural networks by preventing co-adaptation of feature detectors","author":"hinton","year":"2012"},{"key":"ref4","article-title":"Optimal brain damage","author":"lecun","year":"1990","journal-title":"NeurIPS"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2017.2761740"},{"key":"ref6","article-title":"Pruning convolutional neural networks for resource efficient inference","author":"molchanov","year":"2017","journal-title":"ICLRE"},{"key":"ref5","article-title":"Second order derivatives for network pruning: Optimal brain surgeon","author":"hassibi","year":"1993","journal-title":"NeurIPS"},{"key":"ref8","article-title":"Learning structured sparsity in deep neural networks","author":"wen","year":"2016","journal-title":"NeurIPS"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.280"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1145\/2647868.2654889"},{"key":"ref45","article-title":"Learning multiple layers of features from tiny images","author":"krizhevsky","year":"2009","journal-title":"Citeseer Tech Rep"},{"key":"ref48","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2014","journal-title":"Computer Science"},{"key":"ref47","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"NeurIPS"},{"key":"ref42","article-title":"High performance convolutional neural networks for document processing","author":"chellapilla","year":"2006","journal-title":"International Workshop on Frontiers in Handwriting Recognition"},{"key":"ref41","article-title":"Darkrank: Accelerating deep metric learning via cross sample similarities transfer","author":"chen","year":"2018","journal-title":"AAAI"},{"key":"ref44","article-title":"Network trimming: A data-driven neuron pruning approach towards efficient deep architectures","author":"hu","year":"2016"},{"key":"ref43","article-title":"cuDNN: Efficient primitives for deep learning","author":"chetlur","year":"2014"}],"event":{"name":"2019 International Joint Conference on Neural Networks (IJCNN)","location":"Budapest, Hungary","start":{"date-parts":[[2019,7,14]]},"end":{"date-parts":[[2019,7,19]]}},"container-title":["2019 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8840768\/8851681\/08852463.pdf?arnumber=8852463","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,30]],"date-time":"2022-09-30T16:26:43Z","timestamp":1664555203000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8852463\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":49,"URL":"https:\/\/doi.org\/10.1109\/ijcnn.2019.8852463","relation":{},"subject":[],"published":{"date-parts":[[2019,7]]}}}