Convolutional Neural Network Pruning Using Filter Attenuation

Filters are the essential elements in convolutional neural networks (CNNs). Filters generate feature maps and form the main part of the computational and memory requirements of the convolutional networks. 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 abruptly removed. Instead, weak filters are attenuated and gradually removed. In the proposed attenuation approach, there is a chance for weak 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 well based on different pruning criteria, and better results are obtained in comparison with the conventional pruning methods.