Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations

Abstract

In this paper, we will evaluate the performance of graph neural networks in two distinct domains: computer vision and reinforcement learning. In the computer vision section, we seek to learn whether a novel non-redundant representation for images as graphs can improve performance over trivial pixel to node mapping on a graph-level prediction graph, specifically image classification. For the reinforcement learning section, we seek to learn if explicitly modeling solving a Rubik's cube as a graph problem can improve performance over a standard model-free technique with no inductive bias.

The work was done as a project for Neural Networks and Deep Learning course, Fall 2021 offering by Prof. Richard Zemel at Columbia University