Parameter sharing based OneshotNAS approaches can significantly reduce the training cost. However, there are still three issues to be urgently solved in the development of lightweight NAS. Among which, the consistence issue is one of major promblem of weight-sharing NAS. The performance of the network sampled from the supernet is inconsistent with the performance of the same network trained independently. This results in an incorrect evaluation and improper ranking of candidate performance. Track 1 tries to narrow the performance gap between candidates with the parameters extracted from the shared parameters and the same architectures with the parameter trained independently. This track requires participants to submit pre-trained supernet using their own strategies. Then, we will evaluate the performance gap between candidates with the parameters extracted from the submitted supernet and performances provided by NAS-Bench. Evaluation metric for track 1. In this track, we utilize Kendall metric, which is a common measurement of the correlation between two ranking, to evaluate the performance gap.
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