Domain generalization is a sub-field of transfer learning that aims at bridging the gap between two different domains in the absence of any knowledge about the target domain. Our approach tackles the problem of a model's weak generalization when it is trained on a single source domain. From this perspective, we build an ensemble model on top of base deep learning models trained on a single source to enhance the generalization of their collective prediction. The results achieved thus far have demonstrated promising improvements of the ensemble over any of its base learners.
@article{arxiv.2103.10257,
title = {Domain Generalization using Ensemble Learning},
author = {Yusuf Mesbah and Youssef Youssry Ibrahim and Adil Mehood Khan},
journal= {arXiv preprint arXiv:2103.10257},
year = {2021}
}
Comments
11 pages, 3 figures, 4 tables, summited to IntelliSys 2021