Title: A simple neural network module for relational reasoning,
Abstract: Relational reasoning is a central component of generally intelligent
behavior, but has proven difficult for neural networks to learn. In this paper
we describe how to use Relation Networks (RNs) as a simple plug-and-play module
to solve problems that fundamentally hinge on relational reasoning. We tested
RN-augmented networks on three tasks: visual question answering using a
challenging dataset called CLEVR, on which we achieve state-of-the-art,
super-human performance; text-based question answering using the bAbI suite of
tasks; and complex reasoning about dynamic physical systems. Then, using a
curated dataset called Sort-of-CLEVR we show that powerful convolutional
networks do not have a general capacity to solve relational questions, but can
gain this capacity when augmented with RNs. Our work shows how a deep learning
architecture equipped with an RN module can implicitly discover and learn to
reason about entities and their relations.