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Weight sharing based and predictor based methods are two major types of fastneural architecture search methods. In this paper, we propose to jointly useweight sharing and predictor in a unified framework. First, we construct aSuperNet in a weight-sharing way and probabilisticly sample architectures fromthe SuperNet. To increase the correctness of the evaluation of architectures,besides direct evaluation using the inherited weights, we further apply afew-shot predictor to assess the architecture on the other hand. The finalevaluation of the architecture is the combination of direct evaluation, theprediction from the predictor and the cost of the architecture. We regard theevaluation as a reward and apply a self-critical policy gradient approach toupdate the architecture probabilities. To further reduce the side effects ofweight sharing, we propose a weakly weight sharing method by introducinganother HyperNet. We conduct experiments on datasets including CIFAR-10,CIFAR-100 and ImageNet under NATS-Bench, DARTS and MobileNet search space. Theproposed WPNAS method achieves state-of-the-art performance on these datasets.

WPNAS: Neural Architecture Search by jointly using Weight Sharing and Predictor