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We propose using the Wasserstein loss for training in inverse problems. In particular, we consider a Learned Primal-Dual reconstruction scheme for ill-posed inverse problems using the Wasserstein distance as loss function in the learning. This is motivated by miss-alignments in training data, which when using standard mean squared error loss could severely degrade reconstruction quality. We give theoretical results and demonstrate the method for a problem in computerized tomography.
This is poster number 55 in Poster Session