  Model pruning aims to reduce the deep neural network (DNN) model size or
computational overhead. Traditional model pruning methods such as l-1 pruning
that evaluates the channel significance for DNN pay too much attention to the
local analysis of each channel and make use of the magnitude of the entire
feature while ignoring its relevance to the batch normalization (BN) and ReLU
layer after each convolutional operation. To overcome these problems, we
propose a new model pruning method from a new perspective of gradient flow in
this paper. Specifically, we first theoretically analyze the channel's
influence based on Taylor expansion by integrating the effects of BN layer and
ReLU activation function. Then, the incorporation of the first-order Talyor
polynomial of the scaling parameter and the shifting parameter in the BN layer
is suggested to effectively indicate the significance of a channel in a DNN.
Comprehensive experiments on both image classification and image denoising
tasks demonstrate the superiority of the proposed novel theory and scheme. Code
is available at https://github.com/CityU-AIM-Group/GFBS.
