Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations

• URL: http://arxiv.org/abs/2207.12984v1
• Date: Tue, 26 Jul 2022 15:42:08 GMT
• Title: Explaining Deep Neural Networks for Point Clouds using Gradient-based Visualisations
• Authors: Jawad Tayyub, Muhammad Sarmad, Nicolas Sch\"onborn
• Abstract summary: We propose a novel approach to generate coarse visual explanations of networks designed to classify unstructured 3D data. Our method uses gradients flowing back to the final feature map layers and maps these values as contributions of the corresponding points in the input point cloud. The generality of our approach is tested on various point cloud classification networks, including'single object' networks PointNet, PointNet++, DGCNN, and a'scene' network VoteNet.
• Score: 1.2891210250935146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explaining decisions made by deep neural networks is a rapidly advancing research topic. In recent years, several approaches have attempted to provide visual explanations of decisions made by neural networks designed for structured 2D image input data. In this paper, we propose a novel approach to generate coarse visual explanations of networks designed to classify unstructured 3D data, namely point clouds. Our method uses gradients flowing back to the final feature map layers and maps these values as contributions of the corresponding points in the input point cloud. Due to dimensionality disagreement and lack of spatial consistency between input points and final feature maps, our approach combines gradients with points dropping to compute explanations of different parts of the point cloud iteratively. The generality of our approach is tested on various point cloud classification networks, including 'single object' networks PointNet, PointNet++, DGCNN, and a 'scene' network VoteNet. Our method generates symmetric explanation maps that highlight important regions and provide insight into the decision-making process of network architectures. We perform an exhaustive evaluation of trust and interpretability of our explanation method against comparative approaches using quantitative, quantitative and human studies. All our code is implemented in PyTorch and will be made publicly available.

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