Karl Schrader

Position: Research Assistant E-mail: schrader -at- mia.uni-saarland.de (please replace anti-spam -at- by @) Address: Mathematical Image Analysis Group Faculty of Mathematics and Computer Science, Saarland University Campus E1.7 66041 Saarbrücken, Germany Office: Room 4.04 Building E1.7, Saarbrücken Campus see also Contact

• Mathematical Foundations of Deep Learning
• Connections between Partial Differential Equations and Convolutional Neural Networks

Journal Papers

1. P. Peter, K. Schrader, T. Alt, J. Weickert:
   Deep Spatial and Tonal Optimisation for Homogeneous Diffusion Inpainting.
   arXiv:2208.14371 [eess.IV], revised September 2022.
2. T. Alt, K. Schrader, M. Augustin, P. Peter, J. Weickert:
   Connections between Numerical Algorithms for PDEs and Neural Networks.
   Journal of Mathematical Imaging and Vision.
   Invited Paper.
   Also available as arXiv:2107.14742 [math.NA], revised March 2022.
3. T. Alt, K. Schrader, J. Weickert, P. Peter, M. Augustin:
   Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods.
   Research in the Mathematical Sciences.
   Also available as arXiv:2108.13993 [cs.LG], revised March 2022.

Conference Papers

4. K. Schrader, P. Peter, N. Kämper, J. Weickert:
   Efficient neural generation of 4K masks for homogeneous diffusion inpainting.
   To appear in L. Calatroni, M. Donatelli, S. Morigi, M. Prato, M. Santavesaria (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Springer, Cham, 2023.
   Also available as arXiv:2303.10096 [eess.IV], March 2023.
5. K. Schrader, T. Alt, J. Weickert, M. Ertel:
   CNN-based Euler’s Elastica Inpainting with Deep Energy and Deep Image Prior.
   In Proceedings of the 10th European Workshop on Visual Information Processing, Lisbon, 2022.
   Also available as arXiv:2207.07921 [cs.CV], July 2022.
6. T. Alt, P. Peter, J. Weickert, K. Schrader:
   Translating numerical concepts for PDEs into neural architectures.
   In A. Elmoataz, J. Fadili, Y. Quéau, J. Rabin, L. Simon (Eds.): Scale Space and Variational Methods in Computer Vision. Lecture Notes in Computer Science, Springer, Cham, 2021.
   Also available as arXiv:2103.15419 [math.NA], March 2021.

Theses

7. K. Schrader:
   Translating Anisotropic Diffusion into Residual Networks.
   M.Sc. Thesis in Visual Computing,
   Saarland University, Saarbrücken, Germany, October 2020.
   


Teaching

Courses

• Winter term 2020:
  Seminar Deep Learning and Optimisation for Visual Computing
• Summer term 2021:
  Seminar Connections of Deep Learning and PDEs for Visual Computing
  Proseminar Naturinspirierte Optimierung
• Winter term 2021:
  Seminar Milestones and Advances in Image Analysis
  Proseminar Simulation der Welt
• Summer term 2022:
  Lecture Model-driven Deep Learning Lab for Image Analysis



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