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. Code is available at:
https://github.com/guochengqian/TNAS.

TNAS 是新的神经结构搜索方法，它使用架构树和二进制运算树来分解搜索空间并减少探索大小，采用修改后的双层宽度优先搜索在提议的树中发现高性能架构，在四个 GPU 小时内在 NAS-Bench-201 上找到 94.37％的 CIFAR-10 全局最优体系结构和 94.35％的平均测试准确率超过了现有技术水平。