Title: The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification,
Abstract: Artificial neural networks have been successfully applied to a variety of
machine learning tasks, including image recognition, semantic segmentation, and
machine translation. However, few studies fully investigated ensembles of
artificial neural networks. In this work, we investigated multiple widely used
ensemble methods, including unweighted averaging, majority voting, the Bayes
Optimal Classifier, and the (discrete) Super Learner, for image recognition
tasks, with deep neural networks as candidate algorithms. We designed several
experiments, with the candidate algorithms being the same network structure
with different model checkpoints within a single training process, networks
with same structure but trained multiple times stochastically, and networks
with different structure. In addition, we further studied the over-confidence
phenomenon of the neural networks, as well as its impact on the ensemble
methods. Across all of our experiments, the Super Learner achieved best
performance among all the ensemble methods in this study.