  Consistently high data quality is essential for the development of novel loss
functions and architectures in the field of deep learning. The existence of
such data and labels is usually presumed, while acquiring high-quality datasets
is still a major issue in many cases. In real-world datasets we often encounter
ambiguous labels due to subjective annotations by annotators. In our
data-centric approach, we propose a method to relabel such ambiguous labels
instead of implementing the handling of this issue in a neural network. A hard
classification is by definition not enough to capture the real-world ambiguity
of the data. Therefore, we propose our method "Data-Centric Classification &
Clustering (DC3)" which combines semi-supervised classification and clustering.
It automatically estimates the ambiguity of an image and performs a
classification or clustering depending on that ambiguity. DC3 is general in
nature so that it can be used in addition to many Semi-Supervised Learning
(SSL) algorithms. On average, this results in a 7.6% better F1-Score for
classifications and 7.9% lower inner distance of clusters across multiple
evaluated SSL algorithms and datasets. Most importantly, we give a
proof-of-concept that the classifications and clusterings from DC3 are
beneficial as proposals for the manual refinement of such ambiguous labels.
Overall, a combination of SSL with our method DC3 can lead to better handling
of ambiguous labels during the annotation process.
