Robust pruning for efficient CNNs-Reference-Cited by-同舟云学术

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Robust pruning for efficient CNNs

Published:2020-07 Issue: Volume:135 Page:90-98
ISSN:0167-8655
Container-title:Pattern Recognition Letters
language:en
Short-container-title:Pattern Recognition Letters

Author:

Ide Hidenori,Kobayashi Takumi,Watanabe Kenji,Kurita Takio

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference27 articles.

1. Imagenet classification with deep convolutional neural networks;Krizhevsky,2012

2. Very deep convolutional networks for large-scale image recognition;Simonyan,2015

3. Deep residual learning for image recognition.;He,2016

4. Going deeper with convolutions;Szegedy,2015

5. Recent advances in efficient computation of deep convolutional neural networks;Cheng;Front. Inf. Technol. Electron.Eng.,2018

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1. PRF: deep neural network compression by systematic pruning of redundant filters;Neural Computing and Applications;2024-08-14

2. Reducing redundancy in the bottleneck representation of autoencoders;Pattern Recognition Letters;2024-02

3. Learning scalable dynamic filter in convolutional networks;Pattern Recognition Letters;2023-12

4. Layer-Wise Filter Thresholding Based CNN Pruning for Efficient IoT Edge Implementations;Internet of Things. Advances in Information and Communication Technology;2023-10-26

5. A generality hard channel pruning with adaptive compression rate selection for HRNet;Pattern Recognition Letters;2023-04

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