  Neural Architecture Search (NAS) is an automatic technique that can search
for well-performed architectures for a specific task. Although NAS surpasses
human-designed architecture in many fields, the high computational cost of
architecture evaluation it requires hinders its development. A feasible
solution is to directly evaluate some metrics in the initial stage of the
architecture without any training. NAS without training (WOT) score is such a
metric, which estimates the final trained accuracy of the architecture through
the ability to distinguish different inputs in the activation layer. However,
WOT score is not an atomic metric, meaning that it does not represent a
fundamental indicator of the architecture. The contributions of this paper are
in three folds. First, we decouple WOT into two atomic metrics which represent
the distinguishing ability of the network and the number of activation units,
and explore better combination rules named (Distinguishing Activation Score)
DAS. We prove the correctness of decoupling theoretically and confirmed the
effectiveness of the rules experimentally. Second, in order to improve the
prediction accuracy of DAS to meet practical search requirements, we propose a
fast training strategy. When DAS is used in combination with the fast training
strategy, it yields more improvements. Third, we propose a dataset called
Darts-training-bench (DTB), which fills the gap that no training states of
architecture in existing datasets. Our proposed method has 1.04$\times$ -
1.56$\times$ improvements on NAS-Bench-101, Network Design Spaces, and the
proposed DTB.
