Automatic Inference of Cross-modal Connection Topologies for X-CNNs
This addresses the challenge of manual design complexity for researchers and practitioners in machine learning, though it is incremental as it builds on existing cross-modal network concepts.
The paper tackles the problem of reducing design cost and enabling cross-modal convolutional neural networks (X-CNNs) in sparse data environments by introducing automated methods to learn connection topologies from a base CNN and training data, achieving up to 9% accuracy gains over hand-designed variants.
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.