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Neural Architecture Search (NAS) is an automatic technique that can searchfor well-performed architectures for a specific task. Although NAS surpasseshuman-designed architecture in many fields, the high computational cost ofarchitecture evaluation it requires hinders its development. A feasiblesolution is to directly evaluate some metrics in the initial stage of thearchitecture without any training. NAS without training (WOT) score is such ametric, which estimates the final trained accuracy of the architecture throughthe ability to distinguish different inputs in the activation layer. However,WOT score is not an atomic metric, meaning that it does not represent afundamental indicator of the architecture. The contributions of this paper arein three folds. First, we decouple WOT into two atomic metrics which representthe 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 theeffectiveness of the rules experimentally. Second, in order to improve theprediction accuracy of DAS to meet practical search requirements, we propose afast training strategy. When DAS is used in combination with the fast trainingstrategy, it yields more improvements. Third, we propose a dataset calledDarts-training-bench (DTB), which fills the gap that no training states ofarchitecture in existing datasets. Our proposed method has 1.04$\times$ -1.56$\times$ improvements on NAS-Bench-101, Network Design Spaces, and theproposed DTB.

DAS: Neural Architecture Search via Distinguishing Activation Score