  High-quality data is a key aspect of modern machine learning. However, labels
generated by humans suffer from issues like label noise and class ambiguities.
We raise the question of whether hard labels are sufficient to represent the
underlying ground truth distribution in the presence of these inherent
imprecision. Therefore, we compare the disparity of learning with hard and soft
labels quantitatively and qualitatively for a synthetic and a real-world
dataset. We show that the application of soft labels leads to improved
performance and yields a more regular structure of the internal feature space.
