  Convolutional Neural Networks (CNNs) have shown remarkable performance in
general object recognition tasks. In this paper, we propose a new model called
EnsNet which is composed of one base CNN and multiple Fully Connected
SubNetworks (FCSNs). In this model, the set of feature-maps generated by the
last convolutional layer in the base CNN is divided along channels into
disjoint subsets, and these subsets are assigned to the FCSNs. Each of the
FCSNs is trained independent of others so that it can predict the class label
from the subset of the feature-maps assigned to it. The output of the overall
model is determined by majority vote of the base CNN and the FCSNs.
Experimental results using the MNIST, Fashion-MNIST and CIFAR-10 datasets show
that the proposed approach further improves the performance of CNNs. In
particular, an EnsNet achieves a state-of-the-art error rate of 0.16% on MNIST.
