  Inspired by the importance of diversity in biological system, we built an
heterogeneous system that could achieve this goal. Our architecture could be
summarized in two basic steps. First, we generate a diverse set of
classification hypothesis using both Convolutional Neural Networks, currently
the state-of-the-art technique for this task, among with other traditional and
innovative machine learning techniques. Then, we optimally combine them through
Meta-Nets, a family of recently developed and performing ensemble methods.
