  Addressing shifts in data distributions is an important prerequisite for the
deployment of deep learning models to real-world settings. A general approach
to this problem involves the adjustment of models to a new domain through
transfer learning. However, in many cases, this is not applicable in a post-hoc
manner to deployed models and further parameter adjustments jeopardize safety
certifications that were established beforehand. In such a context, we propose
to deal with changes in the data distribution via guided data homogenization
which shifts the burden of adaptation from the model to the data. This approach
makes use of information about the training data contained implicitly in the
deep learning model to learn a domain transfer function. This allows for a
targeted deployment of models to unknown scenarios without changing the model
itself. We demonstrate the potential of data homogenization through experiments
on the CIFAR-10 and MNIST data sets.
