论文标题
图形信号处理和深度学习：卷积，合并和拓扑
Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology
论文作者
论文摘要
深度学习，尤其是卷积神经网络（CNN），在计算机视觉和相关领域取得了快速，显着的改进。但是，当数据具有基础图结构（如社会，生物学和许多其他领域）时，传统的深度学习体系结构的性能很差。本文探讨了1）如何使用图形信号处理（GSP）将CNN组件扩展到图形以提高模型性能； 2）如何根据数据图的拓扑或结构来设计图形CNN体系结构。
Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains. But conventional deep learning architectures perform poorly when data have an underlying graph structure, as in social, biological, and many other domains. This paper explores 1)how graph signal processing (GSP) can be used to extend CNN components to graphs in order to improve model performance; and 2)how to design the graph CNN architecture based on the topology or structure of the data graph.
