Neural architecture search (NAS) is a challenging problem. Hierarchical
search spaces allow for cheap evaluations of neural network sub modules to
serve as surrogate for architecture evaluations. Yet, sometimes the hierarchy
is too restrictive or the surrogate fails to generalize. We present FaDE which
uses differentiable architecture search to obtain relative performance
predictions on finite regions of a hierarchical NAS space. The relative nature
of these ranks calls for a memory-less, batch-wise outer search algorithm for
which we use an evolutionary algorithm with pseudo-gradient descent. FaDE is
especially suited on deep hierarchical, respectively multi-cell search spaces,
which it can explore by linear instead of exponential cost and therefore
eliminates the need for a proxy search space.
Our experiments show that firstly, FaDE-ranks on finite regions of the search
space correlate with corresponding architecture performances and secondly, the
ranks can empower a pseudo-gradient evolutionary search on the complete neural
architecture search space.

FaDE 使用可微分的架构搜索方法，在有限的分层搜索空间上获取相对性能预测，在完整的神经架构搜索空间上实现一个批量式外部搜索算法，实验结果表明 FaDE 在分层和多细胞搜索空间中展开线性探索，消除了代理搜索空间的需要。