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Variational Networks: Connecting Variational Methods and Deep Learning

  • Erich KoblerEmail author
  • Teresa Klatzer
  • Kerstin Hammernik
  • Thomas Pock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10496)

Abstract

In this paper, we introduce variational networks (VNs) for image reconstruction. VNs are fully learned models based on the framework of incremental proximal gradient methods. They provide a natural transition between classical variational methods and state-of-the-art residual neural networks. Due to their incremental nature, VNs are very efficient, but only approximately minimize the underlying variational model. Surprisingly, in our numerical experiments on image reconstruction problems it turns out that giving up exact minimization leads to a consistent performance increase, in particular in the case of convex models.

Notes

Acknowledgements

We acknowledge grant support from the Austrian Science Fund (FWF) under the START project BIVISION, No. Y729 and the European Research Council under the Horizon 2020 program, ERC starting grant HOMOVIS, No. 640156.

Supplementary material

440987_1_En_23_MOESM1_ESM.pdf (1.5 mb)
Supplementary material 1 (pdf 1517 KB)

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Erich Kobler
    • 1
    Email author
  • Teresa Klatzer
    • 1
  • Kerstin Hammernik
    • 1
  • Thomas Pock
    • 1
    • 2
  1. 1.Institute of Computer Graphics and VisionGraz University of TechnologyGrazAustria
  2. 2.Center for Vision, Automation and ControlAustrian Institute of TechnologyViennaAustria

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