Journal of East China Normal University(Natural Science) ›› 2022, Vol. 2022 ›› Issue (4): 31-42.doi: 10.3969/j.issn.1000-5641.2022.04.004

• Computer Science • Previous Articles     Next Articles

Neural architecture search algorithms based on a recursive structure

Jizhou LI, Xin LIN*()   

  1. School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
  • Received:2021-01-07 Online:2022-07-25 Published:2022-07-19
  • Contact: Xin LIN


Neural architecture search algorithms aim to find more efficient neural network structures in a huge neural network structure space using computer heuristic search instead of manual search. Previous studies have addressed the problem of inefficient and time-consuming search for early neural network structures by introducing various constraints on the search space. While constraints on the search space can improve and stabilize the performance of the model, they ignore potentially efficient model structures. Hence, in this study, we constructed a recursive model search space that focuses more on the macroscopic structure of neural networks. We proposed a neural architecture search algorithm that explores this search space through a step-by-step incremental search approach. Experiments showed that the algorithm can efficiently perform neural architecture search tasks in complex search spaces, but still fell slightly short of the latest constrained search space-based neural architecture search algorithms.

Key words: neural architecture search, image classification, convolutional neural network

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