  Deep learning has been successfully applied to many classification problems
including underwater challenges. However, a long-standing issue with deep
learning is the need for large and consistently labeled datasets. Although
current approaches in semi-supervised learning can decrease the required amount
of annotated data by a factor of 10 or even more, this line of research still
uses distinct classes. For underwater classification, and uncurated real-world
datasets in general, clean class boundaries can often not be given due to a
limited information content in the images and transitional stages of the
depicted objects. This leads to different experts having different opinions and
thus producing fuzzy labels which could also be considered ambiguous or
divergent. We propose a novel framework for handling semi-supervised
classifications of such fuzzy labels. It is based on the idea of overclustering
to detect substructures in these fuzzy labels. We propose a novel loss to
improve the overclustering capability of our framework and show the benefit of
overclustering for fuzzy labels. We show that our framework is superior to
previous state-of-the-art semi-supervised methods when applied to real-world
plankton data with fuzzy labels. Moreover, we acquire 5 to 10\% more consistent
predictions of substructures.
