  The algorithms of one-shot neural architecture search(NAS) have been widely
used to reduce computation consumption. However, because of the interference
among the subnets in which weights are shared, the subnets inherited from these
super-net trained by those algorithms have poor consistency in precision
ranking. To address this problem, we propose a step-by-step training super-net
scheme from one-shot NAS to few-shot NAS. In the training scheme, we firstly
train super-net in a one-shot way, and then we disentangle the weights of
super-net by splitting them into multi-subnets and training them gradually.
Finally, our method ranks 4th place in the CVPR2022 3rd Lightweight NAS
Challenge Track1. Our code is available at
https://github.com/liujiawei2333/CVPR2022-NAS-competition-Track-1-4th-solution.
