Learning from Simulated and Unsupervised Images through Adversarial Training

@article{Shrivastava2017LearningFS,
  title={Learning from Simulated and Unsupervised Images through Adversarial Training},
  author={Ashish Shrivastava and Tomas Pfister and Oncel Tuzel and Josh Susskind and Wenda Wang and Russell Webb},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={2242-2251}
}
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 reduce this gap, we propose Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulators output using unlabeled real data, while preserving the… CONTINUE READING
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