When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search

Guocheng Qian, Xuanyang Zhang, Guohao Li, Chen Zhao, Yukang Chen, Xiangyu Zhang, Bernard Ghanem, Jian Sun

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The key challenge in neural architecture search (NAS) is designing how to explore wisely in the huge search space. We propose a new NAS method called TNAS (NAS with trees), which improves search efficiency by exploring only a small number of architectures while also achieving a higher search accuracy. TNAS introduces an architecture tree and a binary operation tree, to factorize the search space and substantially reduce the exploration size. TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture. Impressively, TNAS finds the global optimal architecture on CIFAR-10 with test accuracy of 94.37% in four GPU hours in NAS-Bench-201. The average test accuracy is 94.35%, which outperforms the state-of-the-art.
Original languageEnglish (US)
Title of host publication2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
ISBN (Print)978-1-6654-8740-5
StatePublished - Aug 23 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-09-14
Acknowledgements: This work was done when Guocheng was remotely interned at Megvii technology. This work was also supported by the KAUST Office of Sponsored Research (OSR) through VCC funding.


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