Convolutional Neural Network Pruning with Structural Redundancy Reduction

Understand

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.