• Corpus ID: 239049377

Semi-supervised Domain Adaptation for Semantic Segmentation

@article{Chen2021SemisupervisedDA,
  title={Semi-supervised Domain Adaptation for Semantic Segmentation},
  author={Ying Chen and Xu Ouyang and Kaiyue Zhu and Gady Agam},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.10639}
}
Deep learning approaches for semantic segmentation rely primarily on supervised learning approaches and require substantial efforts in producing pixel-level annotations. Further, such approaches may perform poorly when applied to unseen image domains. To cope with these limitations, both unsupervised domain adaptation (UDA) with full source supervision but without target supervision and semisupervised learning (SSL) with partial supervision have been proposed. While such methods are effective… 

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References

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