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.

本研究针对深度卷积神经网络（CNN）在资源有限硬件上部署困难的问题，提出了一种基于条件互信息的结构化滤波器修剪方法。该方法通过评估各层特征图的重要性，成功减少了模型大小，同时几乎不损失准确性，验证结果显示在CIFAR-10数据集上，VGG16架构的滤波器数量减少了超过三分之一，测试准确率仅下降0.32%。

利用条件互信息修剪深度卷积神经网络