  The goal of filter pruning is to search for unimportant filters to remove in
order to make convolutional neural networks (CNNs) efficient without
sacrificing the performance in the process. The challenge lies in finding
information that can help determine how important or relevant each filter is
with respect to the final output of neural networks. In this work, we share our
observation that the batch normalization (BN) parameters of pre-trained CNNs
can be used to estimate the feature distribution of activation outputs, without
processing of training data. Upon observation, we propose a simple yet
effective filter pruning method by evaluating the importance of each filter
based on the BN parameters of pre-trained CNNs. The experimental results on
CIFAR-10 and ImageNet demonstrate that the proposed method can achieve
outstanding performance with and without fine-tuning in terms of the trade-off
between the accuracy drop and the reduction in computational complexity and
number of parameters of pruned networks.
