  Existing one-shot neural architecture search (NAS) methods have to conduct a
search over a giant super-net, which leads to the huge computational cost. To
reduce such cost, in this paper, we propose a method, called FTSO, to divide
the whole architecture search into two sub-steps. Specifically, in the first
step, we only search for the topology, and in the second step, we search for
the operators. FTSO not only reduces NAS's search time from days to 0.68
seconds, but also significantly improves the found architecture's accuracy. Our
extensive experiments on ImageNet show that within 18 seconds, FTSO can achieve
a 76.4% testing accuracy, 1.5% higher than the SOTA, PC-DARTS. In addition,
FTSO can reach a 97.77% testing accuracy, 0.27% higher than the SOTA, with
nearly 100% (99.8%) search time saved, when searching on CIFAR10.

Computer Vision and Pattern Recognition;Machine Learning