  With a view to bridging the gap between deep learning and symbolic AI, we
present a novel end-to-end neural network architecture that learns to form
propositional representations with an explicitly relational structure from raw
pixel data. In order to evaluate and analyse the architecture, we introduce a
family of simple visual relational reasoning tasks of varying complexity. We
show that the proposed architecture, when pre-trained on a curriculum of such
tasks, learns to generate reusable representations that better facilitate
subsequent learning on previously unseen tasks when compared to a number of
baseline architectures. The workings of a successfully trained model are
visualised to shed some light on how the architecture functions.
