Corpus ID: 198186558

Unsupervised Domain Adaptation via Calibrating Uncertainties

@article{Han2019UnsupervisedDA,
  title={Unsupervised Domain Adaptation via Calibrating Uncertainties},
  author={Ligong Han and Yang Zou and Ruijiang Gao and L. Wang and Dimitris N. Metaxas},
  journal={ArXiv},
  year={2019},
  volume={abs/1907.11202}
}
Unsupervised domain adaptation (UDA) aims at inferring class labels for unlabeled target domain given a related labeled source dataset. Intuitively, a model trained on source domain normally produces higher uncertainties for unseen data. In this work, we build on this assumption and propose to adapt from source to target domain via calibrating their predictive uncertainties. The uncertainty is quantified as the Renyi entropy, from which we propose a general Renyi entropy regularization (RER… Expand
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