Channel Pruning Guided by Classification Loss and Feature Importance
This work addresses model compression for efficient deployment, but it is incremental as it builds on existing layer-by-layer pruning approaches.
The authors tackled the problem of channel pruning in neural networks by introducing a method that incorporates classification loss and feature importance, achieving effective pruning across CIFAR-10, ImageNet, and UCF-101 datasets.
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.