Domain Generalization using Ensemble Learning
This work addresses domain generalization for transfer learning, but it appears incremental as it applies ensemble learning to an existing challenge.
The paper tackles the problem of weak generalization in models trained on a single source domain by building an ensemble model on top of base deep learning models, resulting in promising improvements over individual base learners.
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.