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Pruning is one of the major methods to compress deep neural networks.
“Optimal brain damage,”
Yann LeCun, John S Denker, and Sara A Solla, · 1990
Earlier work this paper cites.
“Learning multiple layers of features from tiny images,”
Alex Krizhevsky, Geoffrey Hinton, et al., · 2009
Earlier work this paper cites.
“Imagenet classification with deep convolutional neural networks,”
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, · 2012
Earlier work this paper cites.
Song Han, Huizi Mao, and William J Dally, · 2015
Earlier work this paper cites.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Earlier work this paper cites.
“Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size,”
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer, · 2016
Cited alongside, same era.
“Structured pruning of deep convolutional neural networks,”
Sajid Anwar, Kyuyeon Hwang, and Wonyong Sung, · 2017
Cited alongside, same era.
“A survey of model compression and acceleration for deep neural networks,”
Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang, · 2017
Cited alongside, same era.
“Micro-differential evolution: Diversity enhancement and a comparative study,”
Hojjat Salehinejad, Shahryar Rahnamayan, and Hamid R Tizhoosh, · 2017
Cited alongside, same era.
“Improved regularization of convolutional neural networks with cutout,”
Terrance DeVries and Graham W Taylor, · 2017
Cited alongside, same era.
“Toward compact convnets via structure-sparsity regularized filter pruning,”
Shaohui Lin, Rongrong Ji, Yuchao Li, Cheng Deng, and Xuelong Li, · 2019
Later among the works it cites.
“Ising-dropout: A regularization method for training and compression of deep neural networks,”
Hojjat Salehinejad and Shahrokh Valaee, · 2019
Later among the works it cites.
“Ising dropout with node grouping for training and compression of deep neural networks,”
Hojjat Salehinejad, Zijian Wang, and Shahrokh Valaee, · 2019
Later among the works it cites.
“Survey of dropout methods for deep neural networks,”
Alex Labach, Hojjat Salehinejad, and Shahrokh Valaee, · 2019
Later among the works it cites.
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