Matching Convolutional Neural Networks without Priors about Data

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

Feb 12, 2018 (modified: Feb 12, 2018) ICLR 2018 Workshop Submission readers: everyone
  • 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