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Artificial neural networks have been successfully applied to a variety ofmachine learning tasks, including image recognition, semantic segmentation, andmachine translation. However, few studies fully investigated ensembles ofartificial neural networks. In this work, we investigated multiple widely usedensemble methods, including unweighted averaging, majority voting, the BayesOptimal Classifier, and the (discrete) Super Learner, for image recognitiontasks, with deep neural networks as candidate algorithms. We designed severalexperiments, with the candidate algorithms being the same network structurewith different model checkpoints within a single training process, networkswith same structure but trained multiple times stochastically, and networkswith different structure. In addition, we further studied the over-confidencephenomenon of the neural networks, as well as its impact on the ensemblemethods. Across all of our experiments, the Super Learner achieved bestperformance among all the ensemble methods in this study.

The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification