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    DeepGCNs: Making GCNs Go as Deep as CNNs

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    Type
    Preprint
    Authors
    Li, Guohao
    Müller, Matthias
    Qian, Guocheng cc
    Delgadillo, Itzel C.
    Abualshour, Abdulellah
    Thabet, Ali Kassem cc
    Ghanem, Bernard cc
    KAUST Department
    Visual Computing Center, KAUST, Thuwal, Saudi Arabia
    Visual Computing Center (VCC)
    Electrical Engineering Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2019-10-15
    Permanent link to this record
    http://hdl.handle.net/10754/660654
    
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    Abstract
    Convolutional Neural Networks (CNNs) have been very successful at solving a variety of computer vision tasks such as object classification and detection, semantic segmentation, activity understanding, to name just a few. One key enabling factor for their great performance has been the ability to train very deep CNNs. Despite their huge success in many tasks, CNNs do not work well with non-Euclidean data which is prevalent in many real-world applications. Graph Convolutional Networks (GCNs) offer an alternative that allows for non-Eucledian data as input to a neural network similar to CNNs. While GCNs already achieve encouraging results, they are currently limited to shallow architectures with 2-4 layers due to vanishing gradients during training. This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs. We show the benefit of deep GCNs with as many as 112 layers experimentally across various datasets and tasks. Specifically, we achieve state-of-the-art performance in part segmentation and semantic segmentation on point clouds and in node classification of protein functions across biological protein-protein interaction (PPI) graphs. We believe that the insights in this work will open a lot of avenues for future research on GCNs and transfer to further tasks not explored in this work. The source code for this work is available for Pytorch and Tensorflow at https://github.com/lightaime/deep_gcns_torch and https://github.com/lightaime/deep_gcns respectively.
    Sponsors
    The authors thank Adel Bibi for his help with the project. This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research through the Visual Computing Center (VCC) funding.
    Publisher
    arXiv
    arXiv
    1910.06849
    Additional Links
    https://arxiv.org/pdf/1910.06849
    Collections
    Preprints; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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