DNAS: A Decoupled Global Neural Architecture Search Method

Abstract

Neural Architecture Search (NAS) can automatically design model architecture with better performance. Current researchers have searched for local architecture similar to block, then stacked to construct entire models, or searched the entire model based on a manually designed benchmark module. There is no method to directly search the architecture of the global(entire) model at the operation level. The purpose of this article is to search the entire model directly in the operation level search space. We analyzed the search space of past methods which searching for local architectures, then a working mode for global model architecture search named CAM is proposed. Proposed CAM decouples the architectural parameters of the entire model which can complete the entire model architecture search with few architecture parameters. In the experiment, the test error 2.68 % in CIFAR-10 is obtained by the proposed method at the global architecture level, which can compare with the stage-of-art local architecture search methods.