System Fusion with Deep Ensembles

Deep neural networks (DNNs) are universal estimators that have achieved state-of-the-art performance in a broad spectrum of classification tasks, opening new perspectives for many applications. One of them is addressing ensemble learning. In this paper, we introduce a set of deep learning techniques for ensemble learning with dense, attention, and convolutional neural network layers. Our approach automatically discovers patterns and correlations between the decisions of individual classifiers, therefore, alleviating the difficulty of building such architectures. To assess its robustness, we evaluate our approach on two complex data sets that target different perspectives of predicting the user perception of multimedia data, i.e., interestingness and violence. The proposed approach outperforms the existing state-of-the-art algorithms by a large margin.