{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T20:55:17Z","timestamp":1762980917158,"version":"3.41.0"},"reference-count":62,"publisher":"IEEE","license":[{"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"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1109\/wacv45572.2020.9093391","type":"proceedings-article","created":{"date-parts":[[2020,5,15]],"date-time":"2020-05-15T03:41:09Z","timestamp":1589514069000},"page":"2998-3007","source":"Crossref","is-referenced-by-count":10,"title":["A \"Network Pruning Network\" Approach to Deep Model Compression"],"prefix":"10.1109","author":[{"given":"Vinay Kumar","family":"Verma","sequence":"first","affiliation":[{"name":"IIT Kanpur,Department of Computer Science and Engineering,India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pravendra","family":"Singh","sequence":"additional","affiliation":[{"name":"IIT Kanpur,Department of Computer Science and Engineering,India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vinay P.","family":"Namboodiri","sequence":"additional","affiliation":[{"name":"IIT Kanpur,Department of Computer Science and Engineering,India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Piyush","family":"Rai","sequence":"additional","affiliation":[{"name":"IIT Kanpur,Department of Computer Science and Engineering,India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Runtime network routing for efficient image classification","author":"rao","year":"2018","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref38","first-page":"6775","article-title":"Structured bayesian pruning via log-normal multiplicative noise","author":"neklyudov","year":"2017","journal-title":"NIPS"},{"article-title":"Cpwc: Contextual point wise convolution for object recognition","year":"2019","author":"mazumder","key":"ref33"},{"key":"ref32","article-title":"Thinet: pruning cnn filters for a thinner net","author":"luo","year":"2018","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"ref31","first-page":"3288","article-title":"Bayesian compression for deep learning","author":"louizos","year":"2017","journal-title":"NIPS"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.298"},{"key":"ref37","article-title":"Pruning convolutional neural networks for resource efficient inference","author":"molchanov","year":"2017","journal-title":"ICLRE"},{"key":"ref36","first-page":"2498","article-title":"Variational dropout sparsifies deep neural networks","author":"molchanov","year":"2017","journal-title":"ICML"},{"article-title":"Efficient estimation of word representations in vector space","year":"2013","author":"mikolov","key":"ref35"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.112"},{"key":"ref60","first-page":"6848","article-title":"Shufflenet: An extremely efficient convolutional neural network for mobile devices","author":"zhang","year":"2018","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"ref62","first-page":"662","article-title":"Less is more: Towards compact cnns","author":"zhou","year":"2016","journal-title":"ECCV"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298809"},{"key":"ref28","first-page":"740","article-title":"Microsoft coco: Common objects in context","author":"lin","year":"2014","journal-title":"ECCV"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.247"},{"key":"ref2","first-page":"2270","article-title":"Learning the number of neurons in deep networks","author":"alvarez","year":"2016","journal-title":"NIPS"},{"article-title":"Structural compression of convolutional neural networks based on greedy filter pruning","year":"2017","author":"abbasi-asl","key":"ref1"},{"key":"ref20","doi-asserted-by":"crossref","DOI":"10.5244\/C.28.88","article-title":"Speeding up convolutional neural networks with low rank expansions","author":"jaderberg","year":"2014"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.280"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref21"},{"key":"ref24","first-page":"598","article-title":"Optimal brain damage","author":"lecun","year":"1990","journal-title":"NIPS"},{"article-title":"Smallify: Learning network size while training","year":"2018","author":"leclerc","key":"ref23"},{"key":"ref26","first-page":"2181","article-title":"Runtime neural pruning","author":"lin","year":"2017"},{"key":"ref25","article-title":"Pruning filters for efficient convnets","author":"li","year":"2017","journal-title":"ICLRE"},{"article-title":"Leveraging filter correlations for deep model compression","year":"2018","author":"singh","key":"ref50"},{"key":"ref51","first-page":"1","article-title":"Hetconv: Beyond homogeneous convolution kernels for deep cnns","author":"singh","year":"2019","journal-title":"International Journal of Computer Vision"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00958"},{"key":"ref58","first-page":"2048","article-title":"Show, attend and tell: Neural image caption generation with visual attention","author":"xu","year":"2015","journal-title":"International Conference on Machine Learning"},{"key":"ref57","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00919"},{"key":"ref56","first-page":"2074","article-title":"Learning structured sparsity in deep neural networks","author":"wen","year":"2016","journal-title":"NIPS"},{"key":"ref55","article-title":"Structured probabilistic pruning for convolutional neural network acceleration","author":"wang","year":"2017","journal-title":"BMVC"},{"key":"ref54","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v34i04.6069","article-title":"A meta-learning framework for generalized zero-shot learning","author":"verma","year":"2020","journal-title":"AAAI"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00450"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/480"},{"key":"ref10","article-title":"Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding","author":"han","year":"2016","journal-title":"ICLRE"},{"key":"ref11","article-title":"Second order derivatives for network pruning: Optimal brain surgeon","author":"hassibi","year":"1993","journal-title":"NIPS"},{"key":"ref40","first-page":"525","article-title":"Xnornet: Imagenet classification using binary convolutional neural networks","author":"rastegari","year":"2016","journal-title":"ECCV"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/309"},{"key":"ref14","article-title":"Amc: Automl for model compression and acceleration on mobile devices","author":"he","year":"2018","journal-title":"the European Conference on Computer Vision (ECCV)"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.155"},{"article-title":"Distilling the knowledge in a neural network","year":"2015","author":"hinton","key":"ref16"},{"article-title":"Network trimming: A data-driven neuron pruning approach towards efficient deep architectures","year":"2016","author":"hu","key":"ref17"},{"article-title":"Densely connected convolutional networks","year":"2016","author":"huang","key":"ref18"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/3212725.3212730"},{"key":"ref4","article-title":"Learning a wavelet-like auto-encoder to accelerate deep neural networks","author":"chen","year":"2018","journal-title":"AAAI"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.279"},{"key":"ref6","article-title":"Exploiting linear structure within convolutional networks for efficient evaluation","author":"denton","year":"2014","journal-title":"NIPS"},{"key":"ref5","first-page":"2285","article-title":"Compressing neural networks with the hashing trick","author":"chen","year":"2015","journal-title":"ICML"},{"key":"ref8","first-page":"1126","article-title":"Model-agnostic meta-learning for fast adaptation of deep networks","volume":"70","author":"finn","year":"2017","journal-title":"Proceedings of the 34th International Conference on Machine Learning"},{"key":"ref7","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":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093378"},{"article-title":"Probabilistic model-agnostic meta-learning","year":"2018","author":"finn","key":"ref9"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00129"},{"key":"ref45","first-page":"103857","article-title":"Falf convnets: Fatuous auxiliary loss based filter-pruning for efficient deep cnns","author":"singh","year":"2019","journal-title":"Image and Vision Computing"},{"article-title":"Accuracy booster: Performance boosting using feature map recalibration","year":"2019","author":"singh","key":"ref48"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00145"},{"key":"ref42","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","author":"ren","year":"2015","journal-title":"NIPS"},{"key":"ref41","article-title":"Few-shot autoregressive density estimation: Towards learning to learn distributions","author":"reed","year":"2018","journal-title":"ICLRE"},{"key":"ref44","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"ICLRE"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"}],"event":{"name":"2020 IEEE Winter Conference on Applications of Computer Vision (WACV)","start":{"date-parts":[[2020,3,1]]},"location":"Snowmass, CO, USA","end":{"date-parts":[[2020,3,5]]}},"container-title":["2020 IEEE Winter Conference on Applications of Computer Vision (WACV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9087828\/9093261\/09093391.pdf?arnumber=9093391","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,27]],"date-time":"2025-05-27T17:07:55Z","timestamp":1748365675000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9093391\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3]]},"references-count":62,"URL":"https:\/\/doi.org\/10.1109\/wacv45572.2020.9093391","relation":{},"subject":[],"published":{"date-parts":[[2020,3]]}}}