深度神经架构搜索综述

深度学习在图像、语音、文本等多种模态的数据任务上取得了优异的效果.然而，针对特定任务，人工设计网络需要花费大量的时间，并且需要设计者具有一定水平的专业知识和设计经验.面对如今日趋复杂的网络架构，仅依靠人工进行设计变得越来越复杂.基于此，借助算法自动地对神经网络进行架构的搜索成为了研究热点.神经架构搜索的方法涉及3个方面：搜索空间、搜索策略、性能评估策略.通过搜索策略在搜索空间中选择一个网络架构，借助性能评估策略对该网络架构进行评估，并将结果反馈给搜索策略指导搜索策略选择更好的网络架构，通过不断迭代得到最优的网络架构.为了更好地为读者提供一个快速了解神经网络架构搜索方法的导航地图，从搜索空间、搜索策略和性能评估策略3个方面对现有典型的神经架构搜索方法进行了梳理，总结讨论了近年来常见的架构搜索方法，并分析了各种方法的优势和不足.

Deep Neural Architecture Search : A Survey

Deep learning has achieved excellent results on data tasks with multiple modalities such as images,speech,and text.However,designing networks manually for specific tasks is time-consuming and requires a certain level of expertise and design experience from the designer.In the face of today s increasingly complex network architectures,relying on manual design alone increasingly becomes complex.For this reason,automatic architecture search of neural networks with the help of algorithms has become a hot research topic.The approach of neural architecture search involves three aspects:search space,search strategy,and performance evaluation strategy.The search strategy samples a network architecture in the search space,evaluates the network architecture by a performance evaluation strategy,and feed-back the results to the search strategy to guide it to select a better network architecture,and obtains the optimal network architecture through continuous iterations.In order to better sort out the methods of neural architecture search,we summarize the common methods in recent years from search space,search strategy and performance evaluation strategy,and analyze their strengths and weaknesses.