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

Improve Ranking Correlation of Super-net through Training Scheme from One-shot NAS to Few-shot NAS