  The required amount of labeled data is one of the biggest issues in deep
learning. Semi-Supervised Learning can potentially solve this issue by using
additional unlabeled data. However, many datasets suffer from variability in
the annotations. The aggregated labels from these annotation are not consistent
between different annotators and thus are considered fuzzy. These fuzzy labels
are often not considered by Semi-Supervised Learning. This leads either to an
inferior performance or to higher initial annotation costs in the complete
machine learning development cycle. We envision the incorporation of fuzzy
labels into Semi-Supervised Learning and give a proof-of-concept of the
potential lower costs and higher consistency in the complete development cycle.
As part of our concept, we discuss current limitations, futures research
opportunities and potential broad impacts.
