Matching Convolutional Neural Networks without Priors about Data

Carlos Eduardo Rosar Kos Lassance, Jean-Charles Vialatte, Vincent Gripon, Nicolas Farrugia

Abstract:

We propose an extension of Convolutional Neural Networks (CNNs) to graph-structured data, including strided convolutions and data augmentation on graph.
Our method matches the accuracy of state-of-the-art CNNs when applied on images, without any prior about their 2D regular structure.
On fMRI data, we obtain a significant gain in accuracy compared with existing graph-based alternatives.

Keywords: irregular domains, convolutional neural networks, graph based convolutional neural networks, graphs, deep learning