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, 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 focus specifically on DNAS and review recent approaches in this field. Furthermore, we propose a novel challenge-based taxonomy to classify DNAS methods. We also discuss the contributions brought to DNAS in the past few years and its impact on the global NAS field. Finally, we conclude by giving some insights into future research directions for the DNAS field.

翻译：在过去几年中，可微分神经网络架构搜索（DNAS）迅速成为自动化深度神经网络架构发现的主流方法。这一崛起主要得益于DARTS（最早的主要DNAS方法之一）的广泛流行。与先前基于强化学习或进化算法的方法相比，DNAS速度快了数个数量级，且占用更少的计算资源。在本篇综合性综述中，我们专门聚焦于DNAS，回顾了该领域近年来的最新方法。此外，我们提出了一种基于挑战的新型分类体系来划分DNAS方法，并探讨了近年来对DNAS的贡献及其对整体NAS领域的影响。最后，我们总结了对DNAS未来研究方向的一些见解。