Evolutionary Neural Architecture Search for Image Restoration
Convolutional neural network (CNN) architectures have traditionally been explored by human experts in a manual search process that is time consuming and ineffectively explores the massive space of potential solutions. Neural architecture search (NAS) methods automatically search the space of neural network hyperparameters in order to find optimal task specific architectures. NAS methods have discovered CNN architectures that achieve state of the art performance in image classification among other tasks, however the application of NAS to image to image regression problems such as image restoration is sparse. This paper proposes a NAS method that performs computation ally efficient evolutionary search of a minimally constrained net work architecture search space. The performance of architectures discovered by the proposed method is evaluated on a variety of image restoration tasks applied to the ImageNet64x64 dataset, and compared with human engineered CNN architectures. The best neural architectures discovered using only 2 GPU hours of evolutionary search exhibit comparable performance to the human engineered baseline architecture.
2019 International Joint Conference on Neural Networks (IJCNN)