English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

Accelerating Convolutional Neural Network Pruning via Spatial Aura Entropy