轻量级神经网络架构综述

Survey of Lightweight Neural Network

深度神经网络已经被证明可以有效地解决图像、自然语言等不同领域的问题.同时,伴随着移动互联网技术的不断发展,便携式设备得到了迅速的普及,用户提出了越来越多的需求.因此,如何设计高效、高性能的轻量级神经网络,是解决问题的关键.详细阐述了3种构建轻量级神经网络的方法,分别是人工设计轻量级神经网络、神经网络模型压缩算法和基于神经网络架构搜索的自动化神经网络架构设计;同时,简要总结和分析了每种方法的特点,并重点介绍了典型的构建轻量级神经网络的算法;最后,总结现有的方法,并给出了未来发展的前景.

Deep neural network has been proved to be effective in solving problems in different fields such as image, natural language, and so on. At the same time, with the continuous development of mobile Internet technology, portable devices have been rapidly popularized, and users have put forward more and more demands. Therefore, how to design an efficient and high performance lightweight neural network is the key to solve the problem. In this paper, three methods of constructing lightweight neural network are described in detail, which are artificial design of lightweight neural network, compression algorithm of neural network model, and automatic neural network architecture design based on searching of neural network architecture. The characteristics of each method are summarized and analyzed briefly, and the typical algorithms of constructing lightweight neural network are introduced emphatically. Finally, the existing methods are summarized and the prospects for future development are given.