  Existing neural architecture search (NAS) methods often return an
architecture with good search performance but generalizes poorly to the test
setting. To achieve better generalization, we propose a novel
neighborhood-aware NAS formulation to identify flat-minima architectures in the
search space, with the assumption that flat minima generalize better than sharp
minima. The phrase ``flat-minima architecture'' refers to architectures whose
performance is stable under small perturbations in the architecture (e.g.,
replacing a convolution with a skip connection). Our formulation takes the
``flatness'' of an architecture into account by aggregating the performance
over the neighborhood of this architecture. We demonstrate a principled way to
apply our formulation to existing search algorithms, including sampling-based
algorithms and gradient-based algorithms. To facilitate the application to
gradient-based algorithms, we also propose a differentiable representation for
the neighborhood of architectures. Based on our formulation, we propose
neighborhood-aware random search (NA-RS) and neighborhood-aware differentiable
architecture search (NA-DARTS). Notably, by simply augmenting DARTS with our
formulation, NA-DARTS outperforms DARTS and achieves state-of-the-art
performance on established benchmarks, including CIFAR-10, CIFAR-100 and
ImageNet.
