Channel Pruning Via Gradient Of Mutual Information For Light-Weight Convolutional Neural Networks

Channel pruning for light-weighting networks is very effective in reducing memory footprint and computational cost. Many channel pruning methods assume that the magnitude of a particular element corresponding to each channel reflects the importance of the channel. Unfortunately, such an assumption does not always hold. To solve this problem, this paper proposes a new method to measure the importance of channels based on gradients of mutual...