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    DeepGCNs: Can GCNs Go As Deep As CNNs?

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    Type
    Conference Paper
    Authors
    Li, Guohao
    Müller, Matthias
    Thabet, Ali Kassem cc
    Ghanem, Bernard cc
    KAUST Department
    King Abdullah University of Science and Technology. KAUST
    Visual Computing Center (VCC)
    Electrical Engineering Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/660662
    
    Metadata
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    Abstract
    Convolutional Neural Networks (CNNs) achieve impressive performance in a wide variety of fields. Their success benefited from a massive boost when very deep CNN models were able to be reliably trained. Despite their merits, 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, borrow concepts from CNNs, and apply them in training. GCNs show promising results, but they are usually limited to very shallow models due to the vanishing gradient problem. As a result, most state-of-the-art GCN models are no deeper than 3 or 4 layers. In this work, we present new ways to successfully train very deep GCNs. We do this by borrowing concepts from CNNs, specifically residual/dense connections and dilated convolutions, and adapting them to GCN architectures. Extensive experiments 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. We believe that the community can greatly benefit from this work, as it opens up many opportunities for advancing GCN-based research.
    Citation
    Li, G., Muller, M., Thabet, A., & Ghanem, B. (2019). DeepGCNs: Can GCNs Go As Deep As CNNs? 2019 IEEE/CVF International Conference on Computer Vision (ICCV). doi:10.1109/iccv.2019.00936
    Publisher
    IEEE
    Conference/Event name
    2019 IEEE/CVF International Conference on Computer Vision (ICCV)
    DOI
    10.1109/ICCV.2019.00936
    arXiv
    1904.03751
    Additional Links
    https://ieeexplore.ieee.org/document/9010334/
    https://ieeexplore.ieee.org/document/9010334/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9010334
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICCV.2019.00936
    Scopus Count
    Collections
    Preprints; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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