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"

9 / 9 papers shown

Title
Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruning Juan Miguel Valverde Artem Shatillo Jussi Tohka AAML 21 5 0 27 Sep 2022
Efficient CNN with uncorrelated Bag of Features pooling Firas Laakom Jenni Raitoharju Alexandros Iosifidis M. Gabbouj 25 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 21 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 19 81 0 11 May 2022
Class-Discriminative CNN Compression Yuchen Liu D. Wentzlaff S. Kung 24 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 22 15 0 10 Nov 2020
Self-grouping Convolutional Neural Networks Qingbei Guo Xiaojun Wu J. Kittler Zhiquan Feng 17 22 0 29 Sep 2020
Learning Filter Basis for Convolutional Neural Network Compression Yawei Li Shuhang Gu Luc Van Gool Radu Timofte SupR 9 97 0 23 Aug 2019
Revisiting the Importance of Individual Units in CNNs via Ablation Bolei Zhou Yiyou Sun David Bau Antonio Torralba FAtt 54 116 0 07 Jun 2018