Abstract
Neural Architecture Search (NAS) finds the best network architecture by exploring the architecture-to-performance manifold. It often trains and evaluates a large number of architectures, causing tremendous computation costs. Recent predictor-based NAS approaches attempt to solve this problem with two key steps: sampling some architecture-performance pairs and fitting a proxy accuracy predictor. Given limited samples, these predictors, however, are far from accurate to locate top architectures. In this paper, we shift the paradigm from finding a complicated predictor that covers the whole architecture space to a set of weaker predictors that progressively move towards the high-performance sub-space. It is based on the key property of the proposed weak predictors that their probabilities of sampling better architectures keep increasing. We thus only sample a few well-performed architectures guided by the previously learned predictor and estimate a new better weak predictor. By this coarse-to-fine iteration, the ranking of sampling space is refined gradually, which helps find the optimal architectures eventually. Experiments demonstrate that our method costs fewer samples to find the top-performance architectures on NAS-Bench-101 and NAS-Bench-201, and it achieves the state-of-the-art ImageNet performance on the NASNet search space. The code is available at this https URL
Abstract (translated)
URL
https://arxiv.org/abs/2102.10490
