Reviews: A simple neural network module for relational reasoning

The paper proposes a plug and play module (called Relation Networks (RNs)) specialized for relational reasoning. The module is composed of Multi Layer Perceptrons and considers relations between all pairs of objects. The proposed module when plugged into traditional networks achieves state of the art performance on the CLEVR visual question answering dataset, state of the art (with joint training for all tasks) on the bAbI textual question answering dataset and high performance (93% on one task and 95% on another) on a newly collected dataset of simulated physical mass-spring systems. The paper also collects a dataset similar to CLEVR to demonstrate the effectiveness of the proposed RNs for relational questions. The proposed Relation Network is a novel neural network specialized for relational reasoning.