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

Stefan Lee; Senthil Purushwalkam; Michael Cogswell; David Crandall; Dhruv Batra

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arXiv · 1511.06314

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

Stefan Lee·Senthil Purushwalkam·Michael Cogswell·David Crandall·Dhruv Batra

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Abstract

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.

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BibTeX RIS

Stefan Lee, Senthil Purushwalkam, Michael Cogswell, David Crandall, Dhruv Batra. 2015-11-19. Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks. https://arxiv.org/abs/1511.06314

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