Neural Architecture Search (NAS) is an important yet challenging task in
network design due to its high computational consumption. To address this
issue, we propose the Reinforced Evolutionary Neural Architecture Search (RE-
NAS), which is an evolutionary method with the reinforced mutation for NAS...
method integrates reinforced mutation into an evolution algorithm for neural
architecture exploration, in which a mutation controller is introduced to learn
the effects of slight modifications and make mutation actions. The reinforced
mutation controller guides the model population to evolve efficiently. Furthermore, as child models can inherit parameters from their parents during
evolution, our method requires very limited computational resources. In
experiments, we conduct the proposed search method on CIFAR-10 and obtain a
powerful network architecture, RENASNet. This architecture achieves a
competitive result on CIFAR-10. The explored network architecture is
transferable to ImageNet and achieves a new state-of-the-art accuracy, i.e.,
75.7% top-1 accuracy with 5.36M parameters on mobile ImageNet. We further test
its performance on semantic segmentation with DeepLabv3 on the PASCAL VOC. RENASNet outperforms MobileNet-v1, MobileNet-v2 and NASNet. It achieves 75.83%
mIOU without being pre-trained on COCO.