Transfer Learning Between Related Tasks Using Expected Label Proportions

@article{Noach2019TransferLB,
  title={Transfer Learning Between Related Tasks Using Expected Label Proportions},
  author={Matan Ben Noach and Yoav Goldberg},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.00430}
}
Deep learning systems thrive on abundance of labeled training data but such data is not always available, calling for alternative methods of supervision. One such method is expectation regularization (XR) (Mann and McCallum, 2007), where models are trained based on expected label proportions. We propose a novel application of the XR framework for transfer learning between related tasks, where knowing the labels of task A provides an estimation of the label proportion of task B. We then use a… CONTINUE READING

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