Unchain the Search Space with Hierarchical Differentiable Architecture Search

TL;DR

This paper introduces Hierarchical Differentiable Architecture Search (H-DAS), a method that searches for both cell-level and stage-level architectures to improve neural network performance without high computational costs.

Contribution

H-DAS extends differentiable architecture search by incorporating hierarchical search at both cell and stage levels, enabling more flexible and effective network designs.

Findings

H-DAS achieves state-of-the-art results on CIFAR10 and ImageNet.

Hierarchical search improves network performance without increasing search cost.

Combining stage-level architectures with existing cell structures boosts accuracy.

Abstract

Differentiable architecture search (DAS) has made great progress in searching for high-performance architectures with reduced computational cost. However, DAS-based methods mainly focus on searching for a repeatable cell structure, which is then stacked sequentially in multiple stages to form the networks. This configuration significantly reduces the search space, and ignores the importance of connections between the cells. To overcome this limitation, in this paper, we propose a Hierarchical Differentiable Architecture Search (H-DAS) that performs architecture search both at the cell level and at the stage level. Specifically, the cell-level search space is relaxed so that the networks can learn stage-specific cell structures. For the stage-level search, we systematically study the architectures of stages, including the number of cells in each stage and the connections between the cells. Based on insightful observations, we design several search rules and losses, and mange to search for better stage-level architectures. Such hierarchical search space greatly improves the performance of the networks without introducing expensive search cost. Extensive experiments on CIFAR10 and ImageNet demonstrate the effectiveness of the proposed H-DAS. Moreover, the searched stage-level architectures can be combined with the cell structures searched by existing DAS methods to further boost the performance. Code is available at: https://github.com/MalongTech/research-HDAS