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.

在过去的几年中，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趋势方法。这一增长主要归功于 DARTS（可微分架构搜索）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，并且使用的计算资源更少。在这项综合调查中，我们特别关注 DNAS 并回顾了该领域的最新方法。此外，我们提出了一种新的基于挑战的分类法来对 DNAS 方法进行分类。我们还讨论了过去几年DNAS带来的贡献以及它对全球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。