We establish a new theoretical framework for learning under multi-class, instance-dependent label noise. This framework casts learning with label noise as a form of domain adaptation, in particular, domain adaptation under posterior drift. We introduce the concept of \emph{relative signal strength} (RSS), a pointwise measure that quantifies the transferability from noisy to clean posterior. Using RSS, we establish nearly matching upper and lower bounds on the excess risk. Our theoretical findings support the simple \emph{Noise Ignorant Empirical Risk Minimization (NI-ERM)} principle, which minimizes empirical risk while ignoring label noise. Finally, we translate this theoretical insight into practice: by using NI-ERM to fit a linear classifier on top of a self-supervised feature extractor, we achieve state-of-the-art performance on the CIFAR-N data challenge.

本研究解决了多类别、实例相关标签噪声下的学习问题，提出了一种新的理论框架，将标签噪声下的学习视为一种领域适应。引入的相对信号强度概念为从噪声到干净后验的可转移性提供了量化指标，并支持了噪声无知经验风险最小化原则。通过将该原则应用于自监督特征提取器的线性分类器，我们在CIFAR-N数据挑战中获得了最先进的表现。

标签噪声：无知即幸福