论文标题
当NAS遇到树木时：用于神经体系结构搜索的有效算法
When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
论文作者
论文摘要
神经建筑搜索（NAS）的主要挑战是设计如何在巨大的搜索空间中明智地探索。我们提出了一种称为TNA（NAS带有树木）的新型NAS方法，该方法仅通过探索少量架构，同时还达到更高的搜索准确性来提高搜索效率。 TNA引入了一个建筑树和二进制操作树，以分解搜索空间并大大降低勘探大小。 TNA在拟议的树木中进行了修改的双层广度优先搜索，以发现高性能的建筑。令人印象深刻的是，TNA在CIFAR-10上找到了全局最佳体系结构，在NAS Bench-201中四个GPU小时内测试精度为94.37 \％。平均测试准确性为94.35 \％，表现优于最先进的。代码可在：\ url {https://github.com/guochengqian/tnas}中获得。
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: \url{https://github.com/guochengqian/TNAS}.
