Convolutional Neural Network Pruning Using Filter Attenuation
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Filters are the essential elements in convolutional neural networks (CNNs).
Filters are corresponded to the feature maps and form the main part of the
computational and memory requirement for the CNN processing. In filter pruning
methods, a filter with all of its components, including channels and
connections, are removed. The removal of a filter can cause a drastic change in
the network's performance. Also, the removed filters cannot come back to the
network structure. We want to address these problems in this paper. We propose
a CNN pruning method based on filter attenuation in which weak filters are not
directly removed. Instead, weak filters are attenuated and gradually removed.
In the proposed attenuation approach, weak filters are not abruptly removed,
and there is a chance for these filters to return to the network. The filter
attenuation method is assessed using the VGG model for the Cifar10 image
classification task. Simulation results show that the filter attenuation works
with different pruning criteria, and better results are obtained in comparison
with the conventional pruning methods.