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Solving Inverse Problems With Deep Neural Networks - Robustness Included?

by Martin Genzel, Jan Macdonald, Maximilian März




IEEE Transactions on Pattern Analysis and Machine Intelligence ( Volume: 45, Issue: 1 )


In the past five years, deep learning methods have become state-of-the-art in solving various inverse problems. Before such approaches can find application in safety-critical fields, a verification of their reliability appears mandatory. Recent works have pointed out instabilities of deep neural networks for several image reconstruction tasks.


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Additional Information

Brief introduction of the dida co-author(s) and relevance for dida's ML developments.

About the Co-Author

During his studies in mathematics (TU Berlin) Jan focussed on applied topics in optimization, functional analysis, and image processing. His doctoral studies (TU Berlin) explored the interplay between theoretical and empirical research on neural networks. This resulted in his PhD thesis investigating the reliability of deep learning for imaging and computer vision tasks in terms of interpretability, robustness, and accuracy. At dida he works as a Machine Learning Researcher at the interface of scientific research and software development.