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SUMMARY:Gitta Kutyniok (Mathematical Institute of the University of Munich
 )
DTSTART:20201202T180000Z
DTEND:20201202T190000Z
DTSTAMP:20260423T003239Z
UID:MPML/18
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/18/">De
 ep Learning meets Physics: Taking the Best out of Both Worlds in Imaging S
 cience</a>\nby Gitta Kutyniok (Mathematical Institute of the University of
  Munich) as part of Mathematics\, Physics and Machine Learning (IST\, Lisb
 on)\n\n\nAbstract\nPure model-based approaches are today often insufficien
 t for solving complex inverse problems in imaging. At the same time\, we w
 itness the tremendous success of data-based methodologies\, in particular\
 , deep neural networks for such problems. However\, pure deep learning app
 roaches often neglect known and valuable information from physics.\n\nIn t
 his talk\, we will provide an introduction to this problem complex and the
 n discuss a general conceptual approach to inverse problems in imaging\, w
 hich combines deep learning and physics. This hybrid approach is based on 
 shearlet-based sparse regularization and deep learning and is guided by a 
 microlocal analysis viewpoint to pay particular attention to the singulari
 ty structures of the data. Finally\, we will present several applications 
 such as tomographic reconstruction and show that our approach outperforms 
 previous methodologies\, including methods entirely based on deep learning
 .\n
LOCATION:https://researchseminars.org/talk/MPML/18/
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