  This paper introduces a way to learn cross-modal convolutional neural network
(X-CNN) architectures from a base convolutional network (CNN) and the training
data to reduce the design cost and enable applying cross-modal networks in
sparse data environments. Two approaches for building X-CNNs are presented. The
base approach learns the topology in a data-driven manner, by using
measurements performed on the base CNN and supplied data. The iterative
approach performs further optimisation of the topology through a combined
learning procedure, simultaneously learning the topology and training the
network. The approaches were evaluated agains examples of hand-designed X-CNNs
and their base variants, showing superior performance and, in some cases,
gaining an additional 9% of accuracy. From further considerations, we conclude
that the presented methodology takes less time than any manual approach would,
whilst also significantly reducing the design complexity. The application of
the methods is fully automated and implemented in Xsertion library.
