Beyond Hard Labels: Investigating data label distributions

• URL: http://arxiv.org/abs/2207.06224v1
• Date: Wed, 13 Jul 2022 14:25:30 GMT
• Title: Beyond Hard Labels: Investigating data label distributions
• Authors: Vasco Grossmann, Lars Schmarje, Reinhard Koch
• Abstract summary: We compare the disparity of learning with hard and soft labels for a synthetic and a real-world dataset. The application of soft labels leads to improved performance and yields a more regular structure of the internal feature space.
• Score: 0.9668407688201357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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