Improve Convolutional Neural Network Pruning by Maximizing Filter Variety

11 March 2022

Papers citing "Improve Convolutional Neural Network Pruning by Maximizing Filter Variety"

12 / 12 papers shown

Title
One-Cycle Pruning: Pruning ConvNets Under a Tight Training Budget Nathan Hubens M. Mancas B. Gosselin Marius Preda T. Zaharia 69 8 0 05 Jul 2021
Movement Pruning: Adaptive Sparsity by Fine-Tuning Victor Sanh Thomas Wolf Alexander M. Rush 79 487 0 15 May 2020
fastai: A Layered API for Deep Learning Jeremy Howard Sylvain Gugger AI4CE 135 871 0 11 Feb 2020
Linear Mode Connectivity and the Lottery Ticket Hypothesis Jonathan Frankle Gintare Karolina Dziugaite Daniel M. Roy Michael Carbin MoMe 163 630 0 11 Dec 2019
The State of Sparsity in Deep Neural Networks Trevor Gale Erich Elsen Sara Hooker 167 763 0 25 Feb 2019
Rethinking the Value of Network Pruning Zhuang Liu Mingjie Sun Tinghui Zhou Gao Huang Trevor Darrell 42 1,477 0 11 Oct 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle Michael Carbin 293 3,489 0 09 Mar 2018
To prune, or not to prune: exploring the efficacy of pruning for model compression Michael Zhu Suyog Gupta 202 1,282 0 05 Oct 2017
Channel Pruning for Accelerating Very Deep Neural Networks Yihui He Xiangyu Zhang Jian Sun 216 2,534 0 19 Jul 2017
Variational Dropout Sparsifies Deep Neural Networks Dmitry Molchanov Arsenii Ashukha Dmitry Vetrov BDL 181 831 0 19 Jan 2017
Pruning Filters for Efficient ConvNets Hao Li Asim Kadav Igor Durdanovic H. Samet H. Graf 3DPC 195 3,707 0 31 Aug 2016
Learning both Weights and Connections for Efficient Neural Networks Song Han Jeff Pool J. Tran W. Dally CVBM 323 6,715 0 08 Jun 2015