论文标题
修剪深神经网络的方法
Methods for Pruning Deep Neural Networks
论文作者
论文摘要
本文介绍了修剪深神经网络的方法的调查。它首先根据所使用的潜在方法对150多种研究进行分类，然后重点关注三类：使用基于幅度的修剪的方法，利用聚类来识别冗余的方法以及使用灵敏度分析来评估修剪效果的方法。提出了这些类别中的一些关键影响研究，以强调基本的方法和结果。大多数研究都呈现出在文献中分布的结果，因为随着时间的流逝，新的算法，算法和数据集已经开发出来，从而在不同的研究中进行了比较。因此，本文为社区提供了资源，可用于快速比较各种数据集的许多不同方法以及包括Alexnet，Resnet，Densenet和VGG在内的一系列架构。通过比较在ImageNet和Resnet56和resnet56和VGG16上在CIFAR10数据上进行修剪的结果和RESNET50所发布的结果来说明资源，以揭示哪种修剪方法在保持准确性方面很好地工作，同时达到良好的压缩率。本文结束了，以确定一些有希望的未来研究方向。
This paper presents a survey of methods for pruning deep neural networks. It begins by categorising over 150 studies based on the underlying approach used and then focuses on three categories: methods that use magnitude based pruning, methods that utilise clustering to identify redundancy, and methods that use sensitivity analysis to assess the effect of pruning. Some of the key influencing studies within these categories are presented to highlight the underlying approaches and results achieved. Most studies present results which are distributed in the literature as new architectures, algorithms and data sets have developed with time, making comparison across different studied difficult. The paper therefore provides a resource for the community that can be used to quickly compare the results from many different methods on a variety of data sets, and a range of architectures, including AlexNet, ResNet, DenseNet and VGG. The resource is illustrated by comparing the results published for pruning AlexNet and ResNet50 on ImageNet and ResNet56 and VGG16 on the CIFAR10 data to reveal which pruning methods work well in terms of retaining accuracy whilst achieving good compression rates. The paper concludes by identifying some promising directions for future research.
