Accelerating Convolutional Neural Network Pruning via Spatial Aura
  Entropy
Bogdan Musat Razvan Andonie
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.