Convolution-Weight-Distribution Assumption: Rethinking the Criteria of
Channel Pruning

Convolution-Weight-Distribution Assumption: Rethinking the Criteria of Channel Pruning

24 April 2020

Zhongzhan Huang

Papers citing "Convolution-Weight-Distribution Assumption: Rethinking the Criteria of Channel Pruning"

10 / 10 papers shown

Title
Generative adversarial learning with optimal input dimension and its adaptive generator architecture Zhiyao Tan Ling Zhou Huazhen Lin GAN 42 0 0 06 May 2024
Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks Jue Chen Huan Yuan Jianchao Tan Bin Chen Chengru Song Di Zhang 25 3 0 09 Aug 2023
Mix-Pooling Strategy for Attention Mechanism Shan Zhong Wushao Wen Jinghui Qin 33 3 0 22 Aug 2022
MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering Rui Wang Xingkai Wang H. Chen Jérémie Decouchant S. Picek Ziqiang Liu K. Liang 38 1 0 22 Aug 2022
Efficient Climate Simulation via Machine Learning Method Xin Wang Wei Xue Yilun Han Guangwen Yang AILaw 28 2 0 15 Aug 2022
Entropy Induced Pruning Framework for Convolutional Neural Networks Yihe Lu Ziyu Guan Yaming Yang Maoguo Gong Wei Zhao Kaiyuan Feng 38 2 0 13 Aug 2022
Revisiting Random Channel Pruning for Neural Network Compression Yawei Li Kamil Adamczewski Wen Li Shuhang Gu Radu Timofte Luc Van Gool 24 81 0 11 May 2022
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression Yawei Li Shuhang Gu Christoph Mayer Luc Van Gool Radu Timofte 137 189 0 19 Mar 2020
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Z. Tu Kaiming He 297 10,225 0 16 Nov 2016
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation Vijay Badrinarayanan Alex Kendall R. Cipolla SSeg 446 15,645 0 02 Nov 2015