论文标题
超越硬标签：研究数据标签分布
Beyond Hard Labels: Investigating data label distributions
论文作者
论文摘要
高质量数据是现代机器学习的关键方面。但是，人类产生的标签遭受了标签噪声和阶级歧义等问题。我们提出了一个问题，即在存在这些固有的不精确的情况下，硬标签是否足以代表基本的地面真相分布。因此，我们将学习的差异与硬性和软标签进行定量和质量，用于合成和现实世界数据集。我们表明，软标签的应用可提高性能，并产生内部特征空间的更常规结构。
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.
