  Humans have the ability to seamlessly combine low-level visual input with
high-level symbolic reasoning often in the form of recognising objects,
learning relations between them and applying rules. Neuro-symbolic systems aim
to bring a unifying approach to connectionist and logic-based principles for
visual processing and abstract reasoning respectively. This paper presents a
complete neuro-symbolic method for processing images into objects, learning
relations and logical rules in an end-to-end fashion. The main contribution is
a differentiable layer in a deep learning architecture from which symbolic
relations and rules can be extracted by pruning and thresholding. We evaluate
our model using two datasets: subgraph isomorphism task for symbolic rule
learning and an image classification domain with compound relations for
learning objects, relations and rules. We demonstrate that our model scales
beyond state-of-the-art symbolic learners and outperforms deep relational
neural network architectures.
