Efficient Automation of Neural Network Design: A Survey on Differentiable Neural Architecture Search

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Alexandre Heuillet, Ahmad Nasser, Hichem Arioui, Hedi Tabia
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引用次数: 0

Abstract

In the past few years, Differentiable Neural Architecture Search (DNAS) rapidly imposed itself as the trending approach to automate the discovery of deep neural network architectures. This rise is mainly due to the popularity of DARTS (Differentiable ARchitecTure Search), one of the first major DNAS methods. In contrast with previous works based on Reinforcement Learning or Evolutionary Algorithms, DNAS is faster by several orders of magnitude and uses fewer computational resources. In this comprehensive survey, we focused specifically on DNAS and reviewed recent approaches in this field. Furthermore, we proposed a novel challenge-based taxonomy to classify DNAS methods. We also discussed the contributions brought to DNAS in the past few years and its impact on the global NAS field. Finally, we concluded by giving some insights into future research directions for the DNAS field.

神经网络设计的高效自动化:可微分神经架构搜索调查
在过去几年中,可微分神经架构搜索(DNAS)迅速成为自动发现深度神经网络架构的潮流方法。这种崛起主要归功于 DARTS(可微分神经架构搜索)的流行,它是最早的主要 DNAS 方法之一。与之前基于强化学习或进化算法的工作相比,DNAS 的速度快了几个数量级,而且使用的计算资源更少。在这份综合调查报告中,我们特别关注 DNAS,并回顾了该领域的最新方法。此外,我们还提出了一种新颖的基于挑战的分类法,用于对 DNAS 方法进行分类。我们还讨论了 DNAS 在过去几年中的贡献及其对全球 NAS 领域的影响。最后,我们对 DNAS 领域未来的研究方向提出了一些见解。
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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