  Convolutional neural network (CNN) pruning has become one of the most
successful network compression approaches in recent years. Existing works on
network pruning usually focus on removing the least important filters in the
network to achieve compact architectures. In this study, we claim that
identifying structural redundancy plays a more essential role than finding
unimportant filters, theoretically and empirically. We first statistically
model the network pruning problem in a redundancy reduction perspective and
find that pruning in the layer(s) with the most structural redundancy
outperforms pruning the least important filters across all layers. Based on
this finding, we then propose a network pruning approach that identifies
structural redundancy of a CNN and prunes filters in the selected layer(s) with
the most redundancy. Experiments on various benchmark network architectures and
datasets show that our proposed approach significantly outperforms the previous
state-of-the-art.
