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SUMMARY:Neural Fields for Embedded Variational Problems in Imaging - Danie
 lle Bednarski (DESY Hamburg)
DTSTART:20241203T130000Z
DTEND:20241203T140000Z
UID:TALK224854@talks.cam.ac.uk
CONTACT:Ferdia Sherry
DESCRIPTION:Calibration-based functional lifting allows us to embed non-co
 nvex variational problems into another space\, such that the embedded and 
 relaxed formulation is convex and that global minimizers of the later can 
 be mapped to global minimizers of the original problem. While related theo
 ry and results in the continuous setting are very elegant\, practical impl
 ementation of the calibration-based lifted formulation comes with certain 
 challenges. \nIn the first part of this talk\, we look at the theoretic de
 rivation and properties of the (continuous) lifting approach and discuss d
 ifficulties encountered using established discretization approaches. \nIn 
 the second part\, we introduce a more recent stochastic optimization appro
 ach using neural fields and discuss its current restrictions and future pr
 ospects. 
LOCATION:MR14 Centre for Mathematical Sciences
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