Special Issue on the Mathematical Foundations of Deep Learning in Imaging Science

Joan Bruna, Eldad Haber, Gitta Kutyniok, René Vidal, Thomas Pock

Research output: Contribution to journalEditorialpeer-review


Deep learning methods have become an omnipresent and highly successful part of recent approaches in imaging and vision. However, in most cases they are used on a purely empirical basis without real understanding of their behavior. From a scientific viewpoint, this is unsatisfying. Many mathematically inclined researchers have a strong desire to understand the theoretical reasons for the success of these approaches and to find relations between deep learning and mathematically well-established techniques in imaging science. The goal of this special issue is to showcase their latest research results and to promote future research in this direction. It features twelve articles. To avoid any conflicts of interest, articles in which one of the guest editors is involved as co-author, have been handled by another guest editor.
Original languageEnglish
Pages (from-to)277-278
Number of pages2
JournalJournal of Mathematical Imaging and Vision
Issue number3
Publication statusPublished - 1 Apr 2020

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Applied Mathematics
  • Geometry and Topology
  • Computer Vision and Pattern Recognition
  • Statistics and Probability
  • Modelling and Simulation

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