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.

翻译：神经架构搜索（NAS）为神经网络（NN）架构的自动定义铺平了道路，吸引了越来越多的研究关注，并在各种场景中提供解决方案。本研究提出了一种名为平坦神经架构搜索（FlatNAS）的新型NAS解决方案，探索了基于权重扰动鲁棒性的新型品质因数与使用锐度感知最小化（SAM）进行单一NN优化之间的相互作用。FlatNAS是文献中首个在NAS过程中系统探索NN损失景观平坦区域的工作，同时联合优化其在分布内数据上的性能、分布外（OOD）鲁棒性，并约束其架构中参数的数量。与当前主要关注OOD算法的研究不同，FlatNAS成功评估了NN架构对OOD鲁棒性的影响，这是机器学习和深度学习实际应用中的关键方面。通过在NAS探索中仅使用分布内数据，FlatNAS在性能、OOD泛化能力和参数数量之间实现了良好折中。利用文献中流行的基准数据集，通过聚焦于输入数据损坏鲁棒性，评估了NAS设计模型的OOD鲁棒性。