  A long-standing issue with deep learning is the need for large and
consistently labeled datasets. Although the current research 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 like cats and
dogs. However, in the real-world we often encounter problems where different
experts have different opinions, thus producing fuzzy labels. We propose a
novel framework for handling semi-supervised classifications of such fuzzy
labels. Our framework 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 on the common image
classification dataset STL-10 that it is faster and has better overclustering
performance than previous work. On a real-world plankton dataset, we illustrate
the benefit of overclustering for fuzzy labels and show that we beat previous
state-of-the-art semisupervised methods. Moreover, we acquire 5 to 10% more
consistent predictions of substructures.
