  We present a new supervised architecture termed Mediated Mixture-of-Experts
(MMoE) that allows us to improve classification accuracy of Deep Convolutional
Networks (DCN). Our architecture achieves this with the help of expert
networks: A network is trained on a disjoint subset of a given dataset and then
run in parallel to other experts during deployment. A mediator is employed if
experts contradict each other. This allows our framework to naturally support
incremental learning, as adding new classes requires (re-)training of the new
expert only. We also propose two measures to control computational complexity:
An early-stopping mechanism halts experts that have low confidence in their
prediction. The system allows to trade-off accuracy and complexity without
further retraining. We also suggest to share low-level convolutional layers
between experts in an effort to avoid computation of a near-duplicate feature
set. We evaluate our system on a popular dataset and report improved accuracy
compared to a single model of same configuration.
