  Ensemble models comprising of deep Convolutional Neural Networks (CNN) have
shown significant improvements in model generalization but at the cost of large
computation and memory requirements. In this paper, we present a framework for
learning compact CNN models with improved classification performance and model
generalization. For this, we propose a CNN architecture of a compact student
model with parallel branches which are trained using ground truth labels and
information from high capacity teacher networks in an ensemble learning
fashion. Our framework provides two main benefits: i) Distilling knowledge from
different teachers into the student network promotes heterogeneity in feature
learning at different branches of the student network and enables the network
to learn diverse solutions to the target problem. ii) Coupling the branches of
the student network through ensembling encourages collaboration and improves
the quality of the final predictions by reducing variance in the network
outputs. Experiments on the well established CIFAR-10 and CIFAR-100 datasets
show that our Ensemble Knowledge Distillation (EKD) improves classification
accuracy and model generalization especially in situations with limited
training data. Experiments also show that our EKD based compact networks
outperform in terms of mean accuracy on the test datasets compared to
state-of-the-art knowledge distillation based methods.
