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In the past few years, Differentiable Neural Architecture Search (DNAS)rapidly imposed itself as the trending approach to automate the discovery ofdeep neural network architectures. This rise is mainly due to the popularity ofDARTS, one of the first major DNAS methods. In contrast with previous worksbased on Reinforcement Learning or Evolutionary Algorithms, DNAS is faster byseveral orders of magnitude and uses fewer computational resources. In thiscomprehensive survey, we focus specifically on DNAS and review recentapproaches in this field. Furthermore, we propose a novel challenge-basedtaxonomy to classify DNAS methods. We also discuss the contributions brought toDNAS in the past few years and its impact on the global NAS field. Finally, weconclude by giving some insights into future research directions for the DNASfield.

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