  Neural Architecture Search (NAS) paves the way for the automatic definition
of Neural Network (NN) architectures, attracting increasing research attention
and offering solutions in various scenarios. This study introduces a novel NAS
solution, called Flat Neural Architecture Search (FlatNAS), which explores the
interplay between a novel figure of merit based on robustness to weight
perturbations and single NN optimization with Sharpness-Aware Minimization
(SAM). FlatNAS is the first work in the literature to systematically explore
flat regions in the loss landscape of NNs in a NAS procedure, while jointly
optimizing their performance on in-distribution data, their out-of-distribution
(OOD) robustness, and constraining the number of parameters in their
architecture. Differently from current studies primarily concentrating on OOD
algorithms, FlatNAS successfully evaluates the impact of NN architectures on
OOD robustness, a crucial aspect in real-world applications of machine and deep
learning. FlatNAS achieves a good trade-off between performance, OOD
generalization, and the number of parameters, by using only in-distribution
data in the NAS exploration. The OOD robustness of the NAS-designed models is
evaluated by focusing on robustness to input data corruptions, using popular
benchmark datasets in the literature.
