  Neural Architecture Search (NAS) is a powerful tool to automatically design
deep neural networks for many tasks, including image classification. Due to the
significant computational burden of the search phase, most NAS methods have
focused so far on small, balanced datasets. All attempts at conducting NAS at
large scale have employed small proxy sets, and then transferred the learned
architectures to larger datasets by replicating or stacking the searched cells.
We propose a NAS method based on polyharmonic splines that can perform search
directly on large scale, imbalanced target datasets. We demonstrate the
effectiveness of our method on the ImageNet22K benchmark[16], which contains 14
million images distributed in a highly imbalanced manner over 21,841
categories. By exploring the search space of the ResNet [23] and Big-Little Net
ResNext [11] architectures directly on ImageNet22K, our polyharmonic splines
NAS method designed a model which achieved a top-1 accuracy of 40.03% on
ImageNet22K, an absolute improvement of 3.13% over the state of the art with
similar global batch size [15].
