Domain-Adversarial Training of Neural Networks
Abstract
We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.
Cite
Text
Ganin et al. "Domain-Adversarial Training of Neural Networks." Journal of Machine Learning Research, 2016.Markdown
[Ganin et al. "Domain-Adversarial Training of Neural Networks." Journal of Machine Learning Research, 2016.](https://mlanthology.org/jmlr/2016/ganin2016jmlr-domainadversarial/)BibTeX
@article{ganin2016jmlr-domainadversarial,
title = {{Domain-Adversarial Training of Neural Networks}},
author = {Ganin, Yaroslav and Ustinova, Evgeniya and Ajakan, Hana and Germain, Pascal and Larochelle, Hugo and Laviolette, François and March, Mario and Lempitsky, Victor},
journal = {Journal of Machine Learning Research},
year = {2016},
pages = {1-35},
volume = {17},
url = {https://mlanthology.org/jmlr/2016/ganin2016jmlr-domainadversarial/}
}