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Bilevel Optimization with Nonsmooth Lower Level Problems

  • Peter OchsEmail author
  • René Ranftl
  • Thomas Brox
  • Thomas Pock
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9087)

Abstract

We consider a bilevel optimization approach for parameter learning in nonsmooth variational models. Existing approaches solve this problem by applying implicit differentiation to a sufficiently smooth approximation of the nondifferentiable lower level problem. We propose an alternative method based on differentiating the iterations of a nonlinear primal–dual algorithm. Our method computes exact (sub)gradients and can be applied also in the nonsmooth setting. We show preliminary results for the case of multi-label image segmentation.

Keywords

Neural Information Processing System Lower Level Problem Bilevel Optimization Dual Algorithm Bregman Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Peter Ochs
    • 1
    Email author
  • René Ranftl
    • 2
  • Thomas Brox
    • 1
  • Thomas Pock
    • 2
    • 3
  1. 1.Computer Vision GroupUniversity of FreiburgFreiburgGermany
  2. 2.Institute for Computer Graphics and VisionGraz University of TechnologyGrazAustria
  3. 3.Digital Safety and Security DepartmentAIT Austrian Institute of Technology GmbHViennaAustria

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