Networks found with Neural Architecture Search (NAS) achieve state-of-the-art
performance in a variety of tasks, out-performing human-designed networks.
However, most NAS methods heavily rely on human-defined assumptions that
constrain the search: architecture's outer-skeletons, number of layers,
parameter heuristics and search spaces. Additionally, common search spaces
consist of repeatable modules (cells) instead of fully exploring the
architecture's search space by designing entire architectures (macro-search).
Imposing such constraints requires deep human expertise and restricts the
search to pre-defined settings. In this paper, we propose LCMNAS, a method that
pushes NAS to less constrained search spaces by performing macro-search without
relying on pre-defined heuristics or bounded search spaces. LCMNAS introduces
three components for the NAS pipeline: i) a method that leverages information
about well-known architectures to autonomously generate complex search spaces
based on Weighted Directed Graphs with hidden properties, ii) an evolutionary
search strategy that generates complete architectures from scratch, and iii) a
mixed-performance estimation approach that combines information about
architectures at initialization stage and lower fidelity estimates to infer
their trainability and capacity to model complex functions. We present
experiments in 13 different data sets showing that LCMNAS is capable of
generating both cell and macro-based architectures with minimal GPU computation
and state-of-the-art results. More, we conduct extensive studies on the
importance of different NAS components in both cell and macro-based settings.
Code for reproducibility is public at this https URL.

本文提出了一种名为 LCMNAS 的方法，它能够在非约束搜索空间条件下执行宏搜索，同时不依赖于预定义的启发式算法或范围限制，并引入三个组成部分来推动神经架构搜索方向的发展：利用有关广泛应用架构的信息，自主生成基于具有隐藏属性的加权有向图的复杂搜索空间的方法，从头开始生成完整的架构的进化搜索策略，以及结合有关初始化阶段和更低保真度估计的信息来推断它们对于建模复杂函数的训练能力和容量情况的混合性能估计方法，在 13 个不同的数据集上进行了实验，显示出 LCMNAS 能够使用最少 GPU 计算生成单元和基于宏的架构，并在各方面都取得了最先进的结果。