  There has been a large literature of neural architecture search, but most
existing work made use of heuristic rules that largely constrained the search
flexibility. In this paper, we first relax these manually designed constraints
and enlarge the search space to contain more than $10^{160}$ candidates. In the
new space, most existing differentiable search methods can fail dramatically.
We then propose a novel algorithm named Gradual One-Level Differentiable Neural
Architecture Search (GOLD-NAS) which introduces a variable resource constraint
to one-level optimization so that the weak operators are gradually pruned out
from the super-network. In standard image classification benchmarks, GOLD-NAS
can find a series of Pareto-optimal architectures within a single search
procedure. Most of the discovered architectures were never studied before, yet
they achieve a nice tradeoff between recognition accuracy and model complexity.
We believe the new space and search algorithm can advance the search of
differentiable NAS.
