Pruning Deep Convolutional Neural Network Using Conditional Mutual Information

TL;DR

This paper introduces a novel CNN pruning method based on Conditional Mutual Information that effectively reduces model size by over a third with minimal accuracy loss, enhancing deployment on resource-limited devices.

Contribution

It proposes a structured filter-pruning approach using CMI to identify and retain the most informative features in CNN layers, enabling efficient model compression.

Findings

Reduces VGG16 filters by over 33%

Maintains accuracy with only 0.32% drop

Enables parallel pruning in both directions

Abstract

Convolutional Neural Networks (CNNs) achieve high performance in image classification tasks but are challenging to deploy on resource-limited hardware due to their large model sizes. To address this issue, we leverage Mutual Information, a metric that provides valuable insights into how deep learning models retain and process information through measuring the shared information between input features or output labels and network layers. In this study, we propose a structured filter-pruning approach for CNNs that identifies and selectively retains the most informative features in each layer. Our approach successively evaluates each layer by ranking the importance of its feature maps based on Conditional Mutual Information (CMI) values, computed using a matrix-based Renyi {\alpha}-order entropy numerical method. We propose several formulations of CMI to capture correlation among features across different layers. We then develop various strategies to determine the cutoff point for CMI values to prune unimportant features. This approach allows parallel pruning in both forward and backward directions and significantly reduces model size while preserving accuracy. Tested on the VGG16 architecture with the CIFAR-10 dataset, the proposed method reduces the number of filters by more than a third, with only a 0.32% drop in test accuracy.