PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization.
by Fabian Altekrüger, Alexander Denker, Paul Hagemann, Johannes Hertrich, Peter Maass, Gabriele Steidl
Year:
2022
Publication:
Inverse Problems
Abstract:
Learning neural networks using only few available information is an important ongoing research topic with tremendous potential for applications. In this paper, we introduce a powerful regularizer for the variational modeling of inverse problems in imaging.
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Brief introduction of the dida co-author(s) and relevance for dida's ML developments.
Fabian Altekrüger (PhD)
During his mathematics studies at TU Berlin, Fabian focused on functional analysis. In his subsequent doctoral research, he worked on the regularization and solution of Bayesian inverse problems in mathematical image processing, combining mathematical methods with neural networks. In this context, Fabian developed and applied conditional generative models, always with an emphasis on the stability and robustness of the methods. At dida, he contributes his skills as a machine learning scientist.