  Neural Architecture Search (NAS) defines the design of Neural Networks as a
search problem. Unfortunately, NAS is computationally intensive because of
various possibilities depending on the number of elements in the design and the
possible connections between them. In this work, we extensively analyze the
role of the dataset size based on several sampling approaches for reducing the
dataset size (unsupervised and supervised cases) as an agnostic approach to
reduce search time. We compared these techniques with four common NAS
approaches in NAS-Bench-201 in roughly 1,400 experiments on CIFAR-100. One of
our surprising findings is that in most cases we can reduce the amount of
training data to 25\%, consequently reducing search time to 25\%, while at the
same time maintaining the same accuracy as if training on the full dataset.
Additionally, some designs derived from subsets out-perform designs derived
from the full dataset by up to 22 p.p. accuracy.
