Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives

@article{Liu2022DeepUD,
  title={Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives},
  author={Xiaofeng Liu and Chaehwa Yoo and Fangxu Xing and Hyejin Oh and Georges El Fakhri and Je-Won Kang and Jonghye Woo},
  journal={ArXiv},
  year={2022},
  volume={abs/2208.07422}
}
Deep learning has become the method of choice to tackle real-world problems in different domains, partly because of its ability to learn from data and achieve impressive performance on a wide range of applications. However, its success usually relies on two assumptions: (i) vast troves of labeled datasets are required for accurate model fitting, and (ii) training and testing data are independent and identically distributed. Its performance on unseen target domains, thus, is not guaranteed… 

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