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Deep neural networks have largely failed to effectively utilize syntheticdata when applied to real images due to the covariate shift problem. In thispaper, we show that by applying a straightforward modification to an existingphotorealistic style transfer algorithm, we achieve state-of-the-artsynthetic-to-real domain adaptation results. We conduct extensive experimentalvalidations on four synthetic-to-real tasks for semantic segmentation andobject detection, and show that our approach exceeds the performance of anycurrent state-of-the-art GAN-based image translation approach as measured bysegmentation and object detection metrics. Furthermore we offer a distancebased analysis of our method which shows a dramatic reduction in FrechetInception distance between the source and target domains, offering aquantitative metric that demonstrates the effectiveness of our algorithm inbridging the synthetic-to-real gap.

Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation