神经网络滤波器剪枝技术研究综述OA

With the improvement of software and hardware resources and computing capabilities,deep neural networks have rapidly developed in various fields such as computer vision,natural language processing,and image generation,leading to breakthroughs in areas such as autonomous driving and medical diagnosis through deep learning.However,as the depth of the model increases,the large number of parameters and the consumption of computing resources cause the model to become too complex to train and deploy in resource-constrained environments.To reduce the complexity of network models and improve their efficiency,researchers have proposed pruning methods to compress and accelerate models by reducing redundant parameters and connections.Filter pruning is one of the most important methods to optimize convolu-tional neural networks.It mainly changes the number of filter groups and feature channels to accelerate the network and does not depend on specific algorithm or hardware platform.So the paper reviews the research progress of filter pruning technology at home and abroad in recent years,classifies and summarizes the filter importance evaluation,pruning and fine-tuning mode design,and summarizes the experiments of the mainstream filter pruning methods to analyze the influ-ence of filter pruning on model accuracy and parameter number,and also discusses the future research direction.