  Neural Architecture Search (NAS) has been widely studied for designing
discriminative deep learning models such as image classification, object
detection, and semantic segmentation. As a large number of priors have been
obtained through the manual design of architectures in the fields, NAS is
usually considered as a supplement approach. In this paper, we have
significantly expanded the application areas of NAS by performing an empirical
study of NAS to search generative models, or specifically, auto-encoder based
universal style transfer, which lacks systematic exploration, if any, from the
architecture search aspect. In our work, we first designed a search space where
common operators for image style transfer such as VGG-based encoders, whitening
and coloring transforms (WCT), convolution kernels, instance normalization
operators, and skip connections were searched in a combinatorial approach. With
a simple yet effective parallel evolutionary NAS algorithm with multiple
objectives, we derived the first group of end-to-end deep networks for
universal photorealistic style transfer. Comparing to random search, a NAS
method that is gaining popularity recently, we demonstrated that carefully
designed search strategy leads to much better architecture design. Finally
compared to existing universal style transfer networks for photorealistic
rendering such as PhotoWCT that stacks multiple well-trained auto-encoders and
WCT transforms in a non-end-to-end manner, the architectures designed by
StyleNAS produce better style-transferred images with details preserving, using
a tiny number of operators/parameters, and enjoying around 500x inference time
speed-up.
