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Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm Read chapter Read first chapter

2018 | OriginalPaper | Chapter

ConvNets Pruning by Feature Maps Selection

Authors : Junhua Zou, Ting Rui, You Zhou, Chengsong Yang, Sai Zhang

Published in: Artificial Intelligence and Robotics

Publisher: Springer International Publishing

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Abstract

Convolutional neural network (CNN) is one of the research focuses in machine learning in the last few years. But as the continuous development of CNN in vision and speech, the number of parameters is also increasing, too. CNN, which has millions of parameters, makes the memory of the model very large, and this impedes its widespread especially in mobile device. Based on the observation above, we not only design a CNN pruning method, where we prune unimportant feature maps, but also propose a separability values based number confirmation method which can relatively determine the appropriate pruning number. Experimental results show that, in the cifar-10 dataset, feature maps in each convolutional layer can be pruned by at least 15.6%, up to 59.7%, and the pruning process will not cause any performance loss. We also proved that the confirmation method is effective by a large number of repeated experiments which gradually prune feature maps of each convolutional layer.

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Metadata
Title
ConvNets Pruning by Feature Maps Selection
Authors
Junhua Zou
Ting Rui
You Zhou
Chengsong Yang
Sai Zhang
Copyright Year
2018
Book
Artificial Intelligence and Robotics

Print ISBN: 978-3-319-69876-2

Electronic ISBN: 978-3-319-69877-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-69877-9_27

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