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

• URL: http://arxiv.org/abs/2203.03457v2
• Date: Tue, 8 Mar 2022 07:06:58 GMT
• Title: Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations
• Authors: Naman Goyal, David Steiner
• Abstract summary: 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.
• Score: 15.256931959393803
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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