Explainer on GNN-based segmentation networks

Abstract

Graph Neural Networks (GNN) are powerful tools for deep learning. Similar to other neural networks, GNNs are complex models, in which humans can’t understand the decision-making procedures of the models. Therefore, it brings the need to explainability of GNNs. Explainability is critical for deep learning to support its predictions. In this paper, we will investigate the Grad-Cam and Integrated-Gradients explaining methods. The Grad-Cam applies a global average pooling over the feature activation mapping, and then which was followed by a ReLU activation to obtain an attribution. The Integrated-Gradients explains models by taking a line integral between the baseline image (a black image) and the source image. We demonstrate how Grad-Cam and the Integrated-Gradients methods explain the graph-deep model in semantic segmentation tasks over the Cityscapes dataset. FCN and LRASSP-MobileNet are used as a comparison to the DualGCN in the experiment to show the explaining effect.