Dynamic Filters in Graph Convolutional Networks

  title={Dynamic Filters in Graph Convolutional Networks},
  author={Nitika Verma and Edmond Boyer and Jakob Verbeek},
Convolutional neural networks (CNNs) have massively impacted visual recognition in 2D images, and are now ubiquitous in state-of-the-art approaches. While CNNs naturally extend to other domains, such as audio and video, where data is also organized in rectangular grids, they do not easily generalize to other types of data such as 3D shape meshes, social network graphs or molecular graphs. To handle such data, we propose a novel graph-convolutional network architecture that builds on a generic… CONTINUE READING


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