  In this paper, we present a general and effective framework for Neural
Architecture Search (NAS), named PredNAS. The motivation is that given a
differentiable performance estimation function, we can directly optimize the
architecture towards higher performance by simple gradient ascent.
Specifically, we adopt a neural predictor as the performance predictor.
Surprisingly, PredNAS can achieve state-of-the-art performances on NAS
benchmarks with only a few training samples (less than 100). To validate the
universality of our method, we also apply our method on large-scale tasks and
compare our method with RegNet on ImageNet and YOLOX on MSCOCO. The results
demonstrate that our PredNAS can explore novel architectures with competitive
performances under specific computational complexity constraints.
