Compact Deep Convolutional Neural Networks With Coarse Pruning

Understand

The learning capability of a neural network improves with increasing depth at higher computational costs.

• Wider layers with dense kernel connectivity patterns furhter increase this cost and may hinder real-time inference.
• We propose feature map and kernel level pruning for reducing the computational complexity of a deep convolutional neural network.
• Pruning feature maps reduces the width of a layer and hence does not need any sparse representation.