论文标题
在标签噪声下学习的Fisher-Rao损失
The Fisher-Rao Loss for Learning under Label Noise
论文作者
论文摘要
当通过经验风险最小化学习时，选择合适的损失功能至关重要。在许多实际情况下，用于训练分类器的数据集可能包含不正确的标签，这促使使用损失功能的兴趣，这些功能本质上可以固有地标记噪声。在本文中，我们研究了Fisher-Rao损失函数，该函数从离散分布的统计歧管中从Fisher-Rao距离出现。在存在标签噪声的情况下，我们得出了性能降解的上限，并分析了此损失的学习速度。与其他常用的损失相比，我们认为Fisher-Rao损失在健壮性和训练动态之间提供了自然的权衡。合成和MNIST数据集的数值实验说明了此性能。
Choosing a suitable loss function is essential when learning by empirical risk minimisation. In many practical cases, the datasets used for training a classifier may contain incorrect labels, which prompts the interest for using loss functions that are inherently robust to label noise. In this paper, we study the Fisher-Rao loss function, which emerges from the Fisher-Rao distance in the statistical manifold of discrete distributions. We derive an upper bound for the performance degradation in the presence of label noise, and analyse the learning speed of this loss. Comparing with other commonly used losses, we argue that the Fisher-Rao loss provides a natural trade-off between robustness and training dynamics. Numerical experiments with synthetic and MNIST datasets illustrate this performance.
