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Learning Posterior Distributions in Underdetermined Inverse Problems

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If you have a question about this talk, please contact Dr Priscilla Canizares .

In recent years, classical knowledge-driven approaches for inverse problems have been complemented by data-driven methods exploiting the power of machine and especially deep learning. Purely data-driven methods, however, come with the drawback of disregarding prior knowledge of the problem even though it has shown to be beneficial to incorporate this knowledge into the problem-solving process.

In this talk, we thus introduce an unpaired learning approach for learning posterior distributions of underdetermined inverse problems. It combines advantages of deep generative modeling with established ideas of knowledge-driven approaches by incorporating prior information about the inverse problem

This talk is part of the Astro Data Science Discussion Group series.

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