  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 computationally
efficient evolutionary search of a minimally constrained network 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.
