COMBINING CONVOLUTIONAL NEURAL NETWORKS AND GRAPH NEURAL NETWORKS FOR IMAGE CLASSIFICATION

Convolutional Neural Networks (CNNs) have dominated the task of imageclassification since 2012. Some key components of their success are that the underlying architecture integrates a set inductive biases such as translational invariance and the training computation can be significantly reduced by employing weight sharing. CNNs are powerful tools for generating new representations of images tailored to a particular task such as classification. However, because each image is passed through the network independent of other images, CNNs are not able to effectively aggregate information between examples. In this thesis, we explore the idea of using Graph Neural Networks (GNNs) in conjunction with CNNs to produce an architecture that has both the representational capacity of a CNN and the ability to aggregate information between examples. Graph Neural Networks apply the concept of convolutions directly on graphs. A result of this is that GNNs are able to learn from the connections between nodes. However, when working with image datasets, there is no obvious choice on how to construct a graph. There are certain heuristics such as ensuring homophily that have empirically been shown to increase the performance of GNNs. In this thesis, we apply different schemes of constructing a graph from image data for the downstream task of image classification and experiment with settings such as using multiple feature spaces and enforcing a bipartite graph structure. We also propose a model that allows for end to end training using CNNs and GNNs with proxies and attention that improves classification accuracy in comparison to a regular CNN.