  Graph Convolutional Networks (GCNs) are a class of general models that can
learn from graph structured data. Despite being general, GCNs are admittedly
inferior to convolutional neural networks (CNNs) when applied to vision tasks,
mainly due to the lack of domain knowledge that is hardcoded into CNNs, such as
spatially oriented translation invariant filters. However, a great advantage of
GCNs is the ability to work on irregular inputs, such as superpixels of images.
This could significantly reduce the computational cost of image reasoning
tasks. Another key advantage inherent to GCNs is the natural ability to model
multirelational data. Building upon these two promising properties, in this
work, we show best practices for designing GCNs for image classification; in
some cases even outperforming CNNs on the MNIST, CIFAR-10 and PASCAL image
datasets.
