Motion is the main extrinsic source for imaging artifacts which can strongly deteriorate image quality and thus impair diagnostic accuracy. Numerous motion correction strategies have been proposed to mitigate or capture the artifacts. These methods have in common that they need to be applied during the actual measurement procedure with already a-priori knowledge about the expected motion type and appearance. We propose the usage of deep neural networks to perform retrospective motion correction in a reference-free setting, i.e. not requiring any a-priori motion information. Feasibility and influences of motion type and origin as well as optimal architecture are investigated.