Convolutional Neural Networks (CNNs) achieve impressive results in a wide variety of fields. Their success benefited from a massive boost with the ability to train very deep CNN models. Despite their positive results, CNNs fail to properly address problems with non-Euclidean data. To overcome this challenge, Graph Convolutional Networks (GCNs) build graphs to represent non-Euclidean data, and borrow concepts from CNNs and apply them to train these models. GCNs show promising results, but they are limited to very shallow models due to the vanishing gradient problem. As a result most state-of-the-art GCN algorithms are no deeper than 3 or 4 layers. In this work, we present new ways to successfully train very deep GCNs. We borrow concepts from CNNs, mainly residual/dense connections and dilated convolutions, and adapt them to GCN architectures. Through extensive experiments, we show the positive effect of these deep GCN frameworks. Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3.7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation. The project website is available at https://sites.google.com/view/deep-gcns

本研究通过将 CNN 的残差连接和扩张卷积应用到 GCN 体系结构中，成功地解决了 GCN 中梯度消失的问题，构建了深层的 GCN，实现了更好的点云语义分割表现。

DeepGCNs: GCN能否像CNN一样深?