A Domain Adaptation Regularization for Denoising Autoencoders

@inproceedings{Clinchant2016ADA,
  title={A Domain Adaptation Regularization for Denoising Autoencoders},
  author={St{\'e}phane Clinchant and Gabriela Csurka and Boris Chidlovskii},
  booktitle={ACL},
  year={2016}
}
Finding domain invariant features is critical for successful domain adaptation and transfer learning. However, in the case of unsupervised adaptation, there is a significant risk of overfitting on source training data. Recently, a regularization for domain adaptation was proposed for deep models by (Ganin and Lempitsky, 2015). We build on their work by suggesting a more appropriate regularization for denoising autoencoders. Our model remains unsupervised and can be computed in a closed form. On… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 10 CITATIONS

Computational Linguistics and Intelligent Text Processing

  • Lecture Notes in Computer Science
  • 2017
VIEW 25 EXCERPTS
CITES BACKGROUND, METHODS & RESULTS
HIGHLY INFLUENCED

Domain Specific Feature Transfer for Hybrid Domain Adaptation

  • 2017 IEEE International Conference on Data Mining (ICDM)
  • 2017
VIEW 5 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Feature Analysis of Marginalized Stacked Denoising Autoenconder for Unsupervised Domain Adaptation

  • IEEE Transactions on Neural Networks and Learning Systems
  • 2018
VIEW 5 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

References

Publications referenced by this paper.

Similar Papers

Loading similar papers…