  Convolutional Neural Networks have achieved state-of-the-art performance on a
wide range of tasks. Most benchmarks are led by ensembles of these powerful
learners, but ensembling is typically treated as a post-hoc procedure
implemented by averaging independently trained models with model variation
induced by bagging or random initialization. In this paper, we rigorously treat
ensembling as a first-class problem to explicitly address the question: what
are the best strategies to create an ensemble? We first compare a large number
of ensembling strategies, and then propose and evaluate novel strategies, such
as parameter sharing (through a new family of models we call TreeNets) as well
as training under ensemble-aware and diversity-encouraging losses. We
demonstrate that TreeNets can improve ensemble performance and that diverse
ensembles can be trained end-to-end under a unified loss, achieving
significantly higher "oracle" accuracies than classical ensembles.

Computer Vision and Pattern Recognition;Machine Learning;Neural and Evolutionary Computing