神经架构搜索综述  

Survey of neural architecture search

作　　者：孙仁科[1] 皇甫志宇 陈虎 李仲年 许新征[1] SUN Renke;HUANGFU Zhiyu;CHEN Hu;LI Zhongnian;XU Xinzheng(School of Compute Science and Technology,China University of Mining and Technology,Xuzhou Jiangsu 221116,China)

机构地区：[1]中国矿业大学计算机科学与技术学院,江苏徐州221116

出　　处：《计算机应用》2024年第10期2983-2994,共12页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61976217);徐州市科技计划项目(KC21193)。

摘　　要：近几年,深度学习因具有强大的表征能力,已经在许多领域中取得了突破性的进展,而神经网络的架构对它的性能至关重要。然而,高性能的神经网络架构设计严重依赖研究人员的先验知识和经验,神经网络参数量庞大,难以设计最优的神经网络架构,因此自动神经架构搜索(NAS)获得了极大的关注。NAS是一种使用机器学习的方法,可以在不需要大量人力的情况下,自动搜索最优网络架构的技术,是未来神经网络设计的重要手段之一。NAS本质上是一个搜索优化问题,通过对搜索空间、搜索策略和性能评估策略的设计,自动搜索最优的网络结构。从搜索空间、搜索策略和性能评估策略这3个方面详细且全面地分析、比较和总结目前NAS的研究进展,方便读者快速了解神经架构搜索的发展过程和各项技术的优缺点,并提出NAS未来可能的研究发展方向。In recent years,deep learning has made breakthroughs in many fields due to its powerful representation capability,and the architecture of neural network is crucial to the final performance.However,the design of highperformance neural network architecture heavily relies on the priori knowledge and experience of the researchers.Because there are a lot of parameters for neural networks,it is difficult to design optimal neural network architecture.Therefore,automated Neural Architecture Search(NAS)gains significant attention.NAS is a technique that uses machine learning to automatically search for optimal network architecture without the need for a lot of human effort,and is an important means of future neural network design.NAS is essentially a search optimization problem,by designing search space,search strategy and performance evaluation strategy,NAS can automatically search the optimal network structure.Detailed and comprehensive analysis,comparison and summary for the latest research progress of NAS were provided from three aspects:search space,search strategy,and performance evaluation strategy,which facilitates readers to quickly understand the development process of NAS.And the future research directions of NAS were proposed.

关 键 词：神经架构搜索 深度学习 机器学习 神经网络 搜索空间 搜索策略 性能评估策略 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]