Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation
This provides a strong, simple baseline for domain adaptation, addressing the covariate shift problem in using synthetic data for real-world applications.
The paper tackled the problem of synthetic-to-real domain adaptation by applying a simple modification to an existing style transfer algorithm, achieving state-of-the-art results on semantic segmentation and object detection tasks with improved metrics.
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.