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Pruning CNN filters via quantifying the importance of deep visual representations / Ali Alqahtani, Xianghua Xie, Mark W. Jones, Ehab Essa, Mark Jones, Ehab Mohamed Mahmoud Essa

Computer Vision and Image Understanding, Volume: 208-209, Start page: 103220

Swansea University Authors: Xianghua Xie, Ali Alqahtani, Mark Jones, Ehab Mohamed Mahmoud Essa

  • Accepted Manuscript under embargo until: 18th May 2022

Abstract

The achievement of convolutional neural networks (CNNs) in a variety of applications is accompanied by a dramatic increase in computational costs and memory requirements. In this paper, we pro- pose a novel framework to measure the importance of individual hidden units by computing a measure of rele...

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Published in: Computer Vision and Image Understanding
ISSN: 1077-3142
Published: Elsevier BV 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa56831
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Abstract: The achievement of convolutional neural networks (CNNs) in a variety of applications is accompanied by a dramatic increase in computational costs and memory requirements. In this paper, we pro- pose a novel framework to measure the importance of individual hidden units by computing a measure of relevance to identify the most critical filters and prune them to compress and accelerate CNNs. Unlike exist- ing methods, we introduce the use of the activation of feature maps to detect valuable information and the essential semantic parts to evaluate the importance of feature maps, inspired by novel neural network inter- pretability. A majority voting technique based on the degree of alignment between a semantic concept and individual hidden unit representations is proposed to quantitatively evaluate the importance of feature maps. We also propose a simple yet effective method to estimate new convolution kernels based on the remaining, crucial channels to accomplish effective CNN compression.
Keywords: Deep learning; Convolutional neural networks; Filter pruning; Model compression
College: College of Science
Start Page: 103220