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It is of great significance to estimate the performance of a given modelarchitecture without training in the application of Neural Architecture Search(NAS) as it may take a lot of time to evaluate the performance of anarchitecture. In this paper, a novel NAS framework called GP-NAS-ensemble isproposed to predict the performance of a neural network architecture with asmall training dataset. We make several improvements on the GP-NAS model tomake it share the advantage of ensemble learning methods. Our method rankssecond in the CVPR2022 second lightweight NAS challenge performance predictiontrack.

GP-NAS-ensemble: a model for NAS Performance Prediction