# Speeding up convolutional networks pruning with coarse ranking

## TL;DR

This paper introduces a fast, general channel pruning framework that uses coarse ranking during fine-tuning to significantly reduce computational costs while maintaining state-of-the-art performance across multiple datasets and architectures.

## Contribution

The paper presents a novel, efficient coarse ranking method for channel pruning that can be integrated with various existing pruning techniques, reducing computation time substantially.

## Key findings

- Achieves near state-of-the-art accuracy with much less computation time.
- Reduces 75% and 54% of total pruning time on specific benchmarks.
- Effective across multiple datasets and network architectures.

## Abstract

Channel-based pruning has achieved significant successes in accelerating deep convolutional neural network, whose pipeline is an iterative three-step procedure: ranking, pruning and fine-tuning. However, this iterative procedure is computationally expensive. In this study, we present a novel computationally efficient channel pruning approach based on the coarse ranking that utilizes the intermediate results during fine-tuning to rank the importance of filters, built upon state-of-the-art works with data-driven ranking criteria. The goal of this work is not to propose a single improved approach built upon a specific channel pruning method, but to introduce a new general framework that works for a series of channel pruning methods. Various benchmark image datasets (CIFAR-10, ImageNet, Birds-200, and Flowers-102) and network architectures (AlexNet and VGG-16) are utilized to evaluate the proposed approach for object classification purpose. Experimental results show that the proposed method can achieve almost identical performance with the corresponding state-of-the-art works (baseline) while our ranking time is negligibly short. In specific, with the proposed method, 75% and 54% of the total computation time for the whole pruning procedure can be reduced for AlexNet on CIFAR-10, and for VGG-16 on ImageNet, respectively. Our approach would significantly facilitate pruning practice, especially on resource-constrained platforms.