Geometric deep learning on graphs and manifolds using mixture model CNNs
This work addresses the challenge of applying deep learning to non-Euclidean structured data, which is important for domains like network analysis and computer graphics, but it is incremental as it builds on existing geometric deep learning approaches.
The authors tackled the problem of generalizing convolutional neural networks to non-Euclidean data like graphs and manifolds, proposing a unified framework that outperforms previous methods on standard tasks in image, graph, and 3D shape analysis.
Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) architectures currently produce state-of-the-art performance on a variety of image analysis tasks such as object detection and recognition. Most of deep learning research has so far focused on dealing with 1D, 2D, or 3D Euclidean-structured data such as acoustic signals, images, or videos. Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics. In this paper, we propose a unified framework allowing to generalize CNN architectures to non-Euclidean domains (graphs and manifolds) and learn local, stationary, and compositional task-specific features. We show that various non-Euclidean CNN methods previously proposed in the literature can be considered as particular instances of our framework. We test the proposed method on standard tasks from the realms of image-, graph- and 3D shape analysis and show that it consistently outperforms previous approaches.