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 this https URL

本文探讨了一种新的神经架构搜索度量方式，发现损失曲面的平整度可以预测神经网络架构的泛化能力，通过在不同的搜索空间进行评估，提出的方法相对于现有的 NAS 方法表现更好，并且在数据分布偏移以及目标检测或语义分割等任务中具有稳健的泛化能力。