Beyond Hard Labels: Investigating data label distributions
This addresses data quality issues in machine learning, but it is incremental as it builds on existing soft label methods.
The paper investigates whether hard labels are sufficient for representing ground truth distributions given label noise and ambiguities, comparing learning with hard and soft labels on synthetic and real-world datasets, showing that soft labels improve performance and yield a more regular feature structure.
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.