  In this work, we propose a new layer-by-layer channel pruning method called
Channel Pruning guided by classification Loss and feature Importance (CPLI). In
contrast to the existing layer-by-layer channel pruning approaches that only
consider how to reconstruct the features from the next layer, our approach
additionally take the classification loss into account in the channel pruning
process. We also observe that some reconstructed features will be removed at
the next pruning stage. So it is unnecessary to reconstruct these features. To
this end, we propose a new strategy to suppress the influence of unimportant
features (i.e., the features will be removed at the next pruning stage). Our
comprehensive experiments on three benchmark datasets, i.e., CIFAR-10,
ImageNet, and UCF-101, demonstrate the effectiveness of our CPLI method.
