Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks

19 Nov 2015Stefan LeeSenthil PurushwalkamMichael CogswellDavid CrandallDhruv Batra

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... (read more)

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