In recent years, pruning has emerged as a popular technique to reduce the computational complexity and memory footprint of Convolutional Neural Network (CNN) models. Mutual Information (MI) has been widely used as a criterion for identifying unimportant filters to prune. However, existing methods for MI computation suffer from high computational cost and sensitivity to noise, leading to suboptimal pruning performance. We propose a novel method to improve MI computation for CNN pruning, using the spatial aura entropy. The spatial aura entropy is useful for evaluating the heterogeneity in the distribution of the neural activations over a neighborhood, providing information about local features. Our method effectively improves the MI computation for CNN pruning, leading to more robust and efficient pruning. Experimental results on the CIFAR-10 benchmark dataset demonstrate the superiority of our approach in terms of pruning performance and computational efficiency.

翻译：近年来，剪枝已成为降低卷积神经网络模型计算复杂度与内存占用的流行技术。互信息作为识别冗余滤波器进行剪枝的准则被广泛采用。然而，现有互信息计算方法存在计算成本高且对噪声敏感的问题，导致剪枝性能次优。本文提出一种利用空间光环熵改进卷积神经网络剪枝互信息计算的新方法。空间光环熵能够有效评估神经激活在邻域内分布的异质性，从而提供局部特征信息。该方法显著提升了卷积神经网络剪枝的互信息计算性能，实现更鲁棒高效的剪枝。在CIFAR-10基准数据集上的实验结果表明，本方法在剪枝性能与计算效率方面均具有优越性。