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    Shape-Tailored Deep Neural Networks

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    Type
    Preprint
    Authors
    Khan, Naeemullah
    Sharma, Angira
    Sundaramoorthi, Ganesh cc
    Torr, Philip H. S.
    KAUST Department
    Electrical Engineering Program
    Visual Computing Center (VCC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Physical Science and Engineering (PSE) Division
    Date
    2021-02-16
    Permanent link to this record
    http://hdl.handle.net/10754/667663
    
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    Abstract
    We present Shape-Tailored Deep Neural Networks (ST-DNN). ST-DNN extend convolutional networks (CNN), which aggregate data from fixed shape (square) neighborhoods, to compute descriptors defined on arbitrarily shaped regions. This is natural for segmentation, where descriptors should describe regions (e.g., of objects) that have diverse shape. We formulate these descriptors through the Poisson partial differential equation (PDE), which can be used to generalize convolution to arbitrary regions. We stack multiple PDE layers to generalize a deep CNN to arbitrary regions, and apply it to segmentation. We show that ST-DNN are covariant to translations and rotations and robust to domain deformations, natural for segmentation, which existing CNN based methods lack. ST-DNN are 3-4 orders of magnitude smaller then CNNs used for segmentation. We show that they exceed segmentation performance compared to state-of-the-art CNN-based descriptors using 2-3 orders smaller training sets on the texture segmentation problem.
    Publisher
    arXiv
    arXiv
    2102.08497
    Additional Links
    https://arxiv.org/pdf/2102.08497.pdf
    Collections
    Preprints; Physical Science and Engineering (PSE) Division; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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