  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.
