  Ensemble learning combines several individual models to obtain better
generalization performance. Currently, deep learning architectures are showing
better performance compared to the shallow or traditional models. Deep ensemble
learning models combine the advantages of both the deep learning models as well
as the ensemble learning such that the final model has better generalization
performance. This paper reviews the state-of-art deep ensemble models and hence
serves as an extensive summary for the researchers. The ensemble models are
broadly categorised into bagging, boosting, stacking, negative correlation
based deep ensemble models, explicit/implicit ensembles,
homogeneous/heterogeneous ensemble, decision fusion strategies based deep
ensemble models. Applications of deep ensemble models in different domains are
also briefly discussed. Finally, we conclude this paper with some potential
future research directions.
