Efficient Model Averaging for Deep Neural Networks

Michael Opitz, Horst Possegger, Horst Bischof

Abstract: Large neural networks trained on small datasets are increasingly prone to overfitting. Traditional machine learning methods can reduce overfitting by employing bagging or boosting to train several diverse models. For large neural networks, however, this is prohibitively expensive. To address this issue, we propose a method to leverage the benefits of ensembles without explicitely training several expensive neural network models. In contrast to Dropout, to encourage diversity of our sub-networks, we propose to maximize diversity of individual networks with a loss function: DivLoss. We demonstrate the effectiveness of DivLoss on the challenging CIFAR datasets.