  Neural Architecture Search (NAS) aims to automatically excavate the optimal
network architecture with superior test performance. Recent neural architecture
search (NAS) approaches rely on validation loss or accuracy to find the
superior network for the target data. In this paper, we investigate a new
neural architecture search measure for excavating architectures with better
generalization. We demonstrate that the flatness of the loss surface can be a
promising proxy for predicting the generalization capability of neural network
architectures. We evaluate our proposed method on various search spaces,
showing similar or even better performance compared to the state-of-the-art NAS
methods. Notably, the resultant architecture found by flatness measure
generalizes robustly to various shifts in data distribution (e.g.
ImageNet-V2,-A,-O), as well as various tasks such as object detection and
semantic segmentation. Code is available at https://github.com/clovaai/GeNAS.
