# Reliable Identification of Redundant Kernels for Convolutional Neural Network Compression

## TL;DR

This paper introduces a layer-wise Ln-norm based pruning criterion for CNNs that more accurately identifies unimportant kernels, leading to effective model compression while maintaining accuracy.

## Contribution

It proposes a novel pruning method using output feature map Ln-norms that outperforms existing kernel-norm-based pruning techniques.

## Key findings

- Outperforms existing kernel-norm-based pruning methods.
- Achieves a good balance between model size and accuracy.
- Demonstrated effectiveness on ImageNet and railway surveillance system.

## Abstract

To compress deep convolutional neural networks (CNNs) with large memory footprint and long inference time, this paper proposes a novel pruning criterion using layer-wised Ln-norm of feature maps. Different from existing pruning criteria, which are mainly based on L1-norm of convolution kernels, the proposed method utilizes Ln-norm of output feature maps after non-linear activations, where n is a variable, increasing from 1 at the first convolution layer to inf at the last convolution layer. With the ability of accurately identifying unimportant convolution kernels, the proposed method achieves a good balance between model size and inference accuracy. The experiments on ImageNet and the successful application in railway surveillance system show that the proposed method outperforms existing kernel-norm-based methods and is generally applicable to any deep neural network with convolution operations.

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Source: https://tomesphere.com/paper/1812.03608