Paper ID: 2301.09231

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

Kunlong Chen, Liu Yang, Yitian Chen, Kunjin Chen, Yidan Xu, Lujun Li

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

Submitted: Jan 23, 2023