论文标题
基于图形的通用神经体系结构编码基于预测变量的NAS
A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS
论文作者
论文摘要
这项工作提出了一种基于图形的新型神经体系结构编码方案，也就是盖茨，以改善基于预测的神经体系结构搜索。具体而言，与现有的基于图的方案不同，大门将操作建模为传播信息的转换，这些信息模仿了神经体系结构的实际数据处理。门是对神经体系结构的更合理的建模，并且可以始终如一地从“ node”和“ Edge On On Edge”单元格搜索空间中编码架构。各种搜索空间的实验结果证实了门在改善性能预测因子方面的有效性。此外，配备了改进的性能预测指标，基于预测指标的神经结构搜索（NAS）流的样品效率也得到了增强。代码可在https://github.com/walkerning/aw_nas上找到。
This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search. Specifically, different from existing graph-based schemes, GATES models the operations as the transformation of the propagating information, which mimics the actual data processing of neural architecture. GATES is a more reasonable modeling of the neural architectures, and can encode architectures from both the "operation on node" and "operation on edge" cell search spaces consistently. Experimental results on various search spaces confirm GATES's effectiveness in improving the performance predictor. Furthermore, equipped with the improved performance predictor, the sample efficiency of the predictor-based neural architecture search (NAS) flow is boosted. Codes are available at https://github.com/walkerning/aw_nas.
