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Kernel-based Translations of Convolutional Networks

Corinne Jones,Vincent Roulet,Z. Harchaoui

Published 2019 in arXiv.org

ABSTRACT

Convolutional Neural Networks, as most artificial neural networks, are commonly viewed as methods different in essence from kernel-based methods. We provide a systematic translation of Convolutional Neural Networks (ConvNets) into their kernel-based counterparts, Convolutional Kernel Networks (CKNs), and demonstrate that this perception is unfounded both formally and empirically. We show that, given a Convolutional Neural Network, we can design a corresponding Convolutional Kernel Network, easily trainable using a new stochastic gradient algorithm based on an accurate gradient computation, that performs on par with its Convolutional Neural Network counterpart. We present experimental results supporting our claims on landmark ConvNet architectures comparing each ConvNet to its CKN counterpart over several parameter settings.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    arXiv.org

  • Publication date

    2019-03-19

  • Fields of study

    Mathematics, Computer Science

  • Identifiers
    arXiv 1903.08131
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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