Simulated+Unsupervised (S+U) learning
automatic refinement of depth:
"learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabeled real data, while preserving the annotation information from the simulator. "
full article: https://arxiv.org/abs/1612.07828