I. Introduction
The need to produce a better result when creating machine learning models is probably the biggest topic right now in machine learning. Improving models by a few percent is sometimes seen as groundbreaking. Even if it is only the case for a few very specific use cases, like defect object detection in quality assurance processes of manufacturing. No one is denying the advantages of optimizing the models for particular domains, where an improvement of a few percent could make a big difference. Another striking challenge in machine learning is the lack of sufficient training data. In general, it can quickly become a problem to create a good machine learning model if there is only a few training data available. On the one hand, this can lead to the problem of over fitting, if the models are trained too much with a small amount of data. On the other hand, and probably seen more often in practice, there simply is not enough data available to learn the general structure of the problem.