2017
Learning From Simulated and Unsupervised Images Through Adversarial Training
CVPR 2017oral
With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations. However, learning from synthetic images may not achieve the desired performance due to a gap between synthetic and real image distributions. To…