  Data-driven techniques for machine vision heavily depend on the training data
to sufficiently resemble the data occurring during test and application.
However, in practice unknown distortion can lead to a domain gap between
training and test data, impeding the performance of a machine vision system.
With our proposed approach this domain gap can be closed by unpaired learning
of the pristine-to-distortion mapping function of the unknown distortion. This
learned mapping function may then be used to emulate the unknown distortion in
the training data. Employing a fixed setup, our approach is independent from
prior knowledge of the distortion. Within this work, we show that we can
effectively learn unknown distortions at arbitrary strengths. When applying our
approach to instance segmentation in an autonomous driving scenario, we achieve
results comparable to an oracle with knowledge of the distortion. An average
gain in mean Average Precision (mAP) of up to 0.19 can be achieved.
