Neural Architecture Search (NAS) methods have shown to output networks that
largely outperform human-designed networks. However, conventional NAS methods
have mostly tackled the single dataset scenario, incuring in a large
computational cost as the procedure has to be run from scratch for every new
dataset. In this work, we focus on predictor-based algorithms and propose a
simple and efficient way of improving their prediction performance when dealing
with data distribution shifts. We exploit the Kronecker-product on the randomly
wired search-space and create a small NAS benchmark composed of networks
trained over four different datasets. To improve the generalization abilities,
we propose GRASP-GCN, a ranking Graph Convolutional Network that takes as
additional input the shape of the layers of the neural networks. GRASP-GCN is
trained with the not-at-convergence accuracies, and improves the
state-of-the-art of 3.3 % for Cifar-10 and increasing moreover the
generalization abilities under data distribution shift.

通过使用 Kronecker 积在随机连接搜索空间上，我们提出了一种简单高效的方法来改善基于预测器的算法在处理数据分布变化时的预测性能，我们创建了一个包含四个不同数据集上训练的网络的小型 NAS 基准测试，进一步提高了 GRASP-GCN 的泛化能力并改进了 Cifar-10 的准确度达到 3.3%。