A differentiable neural architecture search algorithm with architecture norm regularization

Differentiable neural architecture search (DNAS) has emerged as a popular method for finding network architectures by using a gradient-based optimization search strategy. However, there have been issues with the instability of the network architecture search and high model complexity. To address these challenges, this paper introduces a novel differentiable neural architecture search algorithm with architecture norm regularization to enhance the stability of network architecture search. Additionally, a redundant edge pruning algorithm is proposed to reduce the complexity of the final model by pruning redundant edges in the network architecture. Comparative experiments on algorithm performance were conducted using four datasets: CIFAR10, CIFAR100, miniImageNet, and Fetal Heart Standard Plane classification (FHSP). The results demonstrate that, in comparison to several of the latest differentiable neural architecture search algorithms, the proposed algorithm achieved the best overall performance.