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.

翻译：神经架构搜索（NAS）旨在自动挖掘具有优异测试性能的最优网络架构。近期神经架构搜索方法依赖验证损失或准确率来为目标数据挖掘优越网络。本文研究了一种用于挖掘具有更好泛化能力架构的新型神经架构搜索度量。我们证明，损失曲面的平坦度可作为预测神经网络架构泛化能力的有前景的代理指标。我们在多种搜索空间上评估了所提出的方法，结果表明其性能与最先进的NAS方法相当甚至更优。值得注意的是，基于平坦度度量搜索得到的架构对多种数据分布偏移（例如ImageNet-V2、-A、-O）以及目标检测、语义分割等不同任务均展现出鲁棒泛化能力。代码已开源在https://github.com/clovaai/GeNAS。