论文标题
通过无训练的统计数据和计算图聚类减少神经体系结构搜索空间
Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering
论文作者
论文摘要
神经体系结构搜索（NAS）算法的计算需求通常与目标搜索空间的大小成正比。因此，将搜索限制为高质量子集可以大大减少NAS算法的计算负载。在本文中，我们提出了基于聚类的减少（C-BRED），这是一种降低NAS搜索空间尺寸的新技术。 C-bred通过聚集与其架构关联的计算图并使用代理统计信息与网络准确性相关的最有希望的群集来减少NAS空间。在考虑NAS-Bench-201（NB201）数据集和CIFAR-100任务时，C-BRED选择一个平均准确性70％的子集，而不是整个空间的平均准确性64％。
The computational demands of neural architecture search (NAS) algorithms are usually directly proportional to the size of their target search spaces. Thus, limiting the search to high-quality subsets can greatly reduce the computational load of NAS algorithms. In this paper, we present Clustering-Based REDuction (C-BRED), a new technique to reduce the size of NAS search spaces. C-BRED reduces a NAS space by clustering the computational graphs associated with its architectures and selecting the most promising cluster using proxy statistics correlated with network accuracy. When considering the NAS-Bench-201 (NB201) data set and the CIFAR-100 task, C-BRED selects a subset with 70% average accuracy instead of the whole space's 64% average accuracy.
