Neural Network Compression by Filter Similarity Detection and Visualization

Abstract

This paper presents an Automated Filter Similarity Detecting and Pruning System with deep visualization technique. During vision tasks, neural networks are found to converge to similar filters leading to significant increase in redundancy in parametric space resulting in high consumption of time and processing power. Moreover, the inner-working of neural networks are not transparent, so it is not tractable which features are extracted in which layer, how they are extracted and how much viable those extracted features are for final output. In order to mitigate the parametric redundancy, we introduce a technique to visualize and detect the similar filters based on similarity metric. And also we implemented the compressed and efficient architecture following pruning, and we observe no decline in learning performance when applied on standard image dataset.