  Neural Architecture Search (NAS) is a powerful tool for automating effective
image processing DNN designing. The ranking has been advocated to design an
efficient performance predictor for NAS. The previous contrastive method solves
the ranking problem by comparing pairs of architectures and predicting their
relative performance. However, it only focuses on the rankings between two
involved architectures and neglects the overall quality distributions of the
search space, which may suffer generalization issues. A predictor, namely
Neural Architecture Ranker (NAR) which concentrates on the global quality tier
of specific architecture, is proposed to tackle such problems caused by the
local perspective. The NAR explores the quality tiers of the search space
globally and classifies each individual to the tier they belong to according to
its global ranking. Thus, the predictor gains the knowledge of the performance
distributions of the search space which helps to generalize its ranking ability
to the datasets more easily. Meanwhile, the global quality distribution
facilitates the search phase by directly sampling candidates according to the
statistics of quality tiers, which is free of training a search algorithm,
e.g., Reinforcement Learning (RL) or Evolutionary Algorithm (EA), thus it
simplifies the NAS pipeline and saves the computational overheads. The proposed
NAR achieves better performance than the state-of-the-art methods on two widely
used datasets for NAS research. On the vast search space of NAS-Bench-101, the
NAR easily finds the architecture with top 0.01$\unicode{x2030}$ performance
only by sampling. It also generalizes well to different image datasets of
NAS-Bench-201, i.e., CIFAR-10, CIFAR-100, and ImageNet-16-120 by identifying
the optimal architectures for each of them.
