In this paper, we study a method to learn the model architectures directly on the dataset of interest. In our experiments, we search for the best convolutional layer (or "cell") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, named "NASNet architecture". For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Image Classification||ImageNet||NASNET-A(6)||Top 1 Accuracy||82.7%||# 3|
|Image Classification||ImageNet||NASNET-A(6)||Top 5 Accuracy||96.2%||# 3|