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

Automatic Inference of Cross-modal Connection Topologies for X-CNNs