Self-ensembling for visual domain adaptation
Geoff French, Michal Mackiewicz, Mark Fisher
Abstract
This paper explores the use of self-ensembling for visual domain adaptation problems. Our technique is derived from the mean teacher variant (Tarvainen et. al 2017) of temporal ensembling (Laine et al. 2017), a technique that achieved state of the art results in the area of semi-supervised learning. We introduce a number of modifications to their approach for challenging domain adaptation scenarios and evaluate its effectiveness. Our approach achieves state of the art results in a variety of benchmarks, including our winning entry in the VISDA-2017 visual domain adaptation challenge. In small image benchmarks, our algorithm not only outperforms prior art, but can also achieve accuracy that is close to that of a classifier trained in a supervised fashion.
BibTeX
@inproceedings{
french2018selfensembling,
title={Self-ensembling for visual domain adaptation},
author={Geoff French and Michal Mackiewicz and Mark Fisher},
booktitle={International Conference on Learning Representations},
year={2018},
url={https://openreview.net/forum?id=rkpoTaxA-},
}