Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation

@article{Yoon2021SemisupervisedDA,
  title={Semi-supervised Domain Adaptation via Sample-to-Sample Self-Distillation},
  author={Jeongbeen Yoon and Dahyun Kang and Minsu Cho},
  journal={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2021},
  pages={1686-1695}
}
Semi-supervised domain adaptation (SSDA) is to adapt a learner to a new domain with only a small set of labeled samples when a large labeled dataset is given on a source domain. In this paper, we propose a pair-based SSDA method that adapts a model to the target domain using self-distillation with sample pairs. Each sample pair is composed of a teacher sample from a labeled dataset (i.e., source or labeled target) and its student sample from an unlabeled dataset (i.e., unlabeled target). Our… 

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