Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression

Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression

11 December 2018

Yongjian Wu

Papers citing "Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression"

12 / 12 papers shown

Title
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning Shangli Zhou Mikhail A. Bragin Lynn Pepin Deniz Gurevin Fei Miao Caiwen Ding 14 3 0 08 Apr 2023
Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning Juan Miguel Valverde Artem Shatillo Jussi Tohka AAML 26 5 0 27 Sep 2022
Efficient CNN with uncorrelated Bag of Features pooling Firas Laakom Jenni Raitoharju Alexandros Iosifidis M. Gabbouj 27 2 0 22 Sep 2022
Survey: Exploiting Data Redundancy for Optimization of Deep Learning Jou-An Chen Wei Niu Bin Ren Yanzhi Wang Xipeng Shen 23 24 0 29 Aug 2022
Revisiting Random Channel Pruning for Neural Network Compression Yawei Li Kamil Adamczewski Wen Li Shuhang Gu Radu Timofte Luc Van Gool 21 81 0 11 May 2022
Class-Discriminative CNN Compression Yuchen Liu D. Wentzlaff S. Kung 26 1 0 21 Oct 2021
Neural Network Compression Via Sparse Optimization Tianyi Chen Bo Ji Yixin Shi Tianyu Ding Biyi Fang Sheng Yi Xiao Tu 30 15 0 10 Nov 2020
Self-grouping Convolutional Neural Networks Qingbei Guo Xiaojun Wu J. Kittler Zhiquan Feng 25 22 0 29 Sep 2020
Dynamic Group Convolution for Accelerating Convolutional Neural Networks Z. Su Linpu Fang Wenxiong Kang D. Hu M. Pietikäinen Li Liu 6 44 0 08 Jul 2020
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression Yawei Li Shuhang Gu Christoph Mayer Luc Van Gool Radu Timofte 123 189 0 19 Mar 2020
Learning Filter Basis for Convolutional Neural Network Compression Yawei Li Shuhang Gu Luc Van Gool Radu Timofte SupR 14 97 0 23 Aug 2019
Revisiting the Importance of Individual Units in CNNs via Ablation Bolei Zhou Yiyou Sun David Bau Antonio Torralba FAtt 56 116 0 07 Jun 2018