  Deep neural networks have largely failed to effectively utilize synthetic
data when applied to real images due to the covariate shift problem. In this
paper, we show that by applying a straightforward modification to an existing
photorealistic style transfer algorithm, we achieve state-of-the-art
synthetic-to-real domain adaptation results. We conduct extensive experimental
validations on four synthetic-to-real tasks for semantic segmentation and
object detection, and show that our approach exceeds the performance of any
current state-of-the-art GAN-based image translation approach as measured by
segmentation and object detection metrics. Furthermore we offer a distance
based analysis of our method which shows a dramatic reduction in Frechet
Inception distance between the source and target domains, offering a
quantitative metric that demonstrates the effectiveness of our algorithm in
bridging the synthetic-to-real gap.
