Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models

@article{Gmez2022CoTrainingFU,
  title={Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models},
  author={Jose Luis G{\'o}mez and Gabriel Villalonga and Antonio M. L'opez},
  journal={Sensors (Basel, Switzerland)},
  year={2022},
  volume={23}
}
Semantic image segmentation is a core task for autonomous driving, which is performed by deep models. Since training these models draws to a curse of human-based image labeling, the use of synthetic images with automatically generated labels together with unlabeled real-world images is a promising alternative. This implies addressing an unsupervised domain adaptation (UDA) problem. In this paper, we propose a new co-training procedure for synth-to-real UDA of semantic segmentation models. It… 

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