  Accuracy predictor is a key component in Neural Architecture Search (NAS) for
ranking architectures. Building a high-quality accuracy predictor usually costs
enormous computation. To address this issue, instead of using an accuracy
predictor, we propose a novel zero-shot index dubbed Zen-Score to rank the
architectures. The Zen-Score represents the network expressivity and positively
correlates with the model accuracy. The calculation of Zen-Score only takes a
few forward inferences through a randomly initialized network, without training
network parameters. Built upon the Zen-Score, we further propose a new NAS
algorithm, termed as Zen-NAS, by maximizing the Zen-Score of the target network
under given inference budgets. Within less than half GPU day, Zen-NAS is able
to directly search high performance architectures in a data-free style.
Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times
faster on multiple server-side and mobile-side GPU platforms with
state-of-the-art accuracy on ImageNet. Our source code and pre-trained models
are released on https://github.com/idstcv/ZenNAS.
