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.

该研究通过探究多种集成方法，包括超级学习器等，运用人工神经网络在图像识别任务中进行设计多个实验，并进一步研究了神经网络的过度自信现象及其对集成方法的影响，并在所有实验中发现超级学习器是表现最佳的集成方法。