Domain Generalization vs Data Augmentation: An Unbiased Perspective
Abstract
In domain generalization the target domain is not known at training time. We show that a style transfer based data augmentation strategy can be implemented easily and outperforms the current state of the art domain generalization methods. Moreover, we observe that those methods, even if combined with the described data augmentation, do not take advantage of it, indicating the need of new generalization solutions.
Cite
Text
Borlino et al. "Domain Generalization vs Data Augmentation: An Unbiased Perspective." European Conference on Computer Vision Workshops, 2020. doi:10.1007/978-3-030-66415-2_50Markdown
[Borlino et al. "Domain Generalization vs Data Augmentation: An Unbiased Perspective." European Conference on Computer Vision Workshops, 2020.](https://mlanthology.org/eccvw/2020/borlino2020eccvw-domain/) doi:10.1007/978-3-030-66415-2_50BibTeX
@inproceedings{borlino2020eccvw-domain,
title = {{Domain Generalization vs Data Augmentation: An Unbiased Perspective}},
author = {Borlino, Francesco Cappio and D'Innocente, Antonio and Tommasi, Tatiana},
booktitle = {European Conference on Computer Vision Workshops},
year = {2020},
pages = {726-730},
doi = {10.1007/978-3-030-66415-2_50},
url = {https://mlanthology.org/eccvw/2020/borlino2020eccvw-domain/}
}