This paper aims to provide understandings for the effect of an over-parameterized model, e.g. a deep neural network, memorizing instance-dependent noisy labels. We first quantify the harms caused by memorizing noisy instances from different spectra of the sample distribution. We then analyze how several popular solutions for learning with noisy labels mitigate this harm at the instance-level. Our analysis reveals new understandings for when these approaches work, and provides theoretical justifications for previously reported empirical observations. A key aspect of the analysis is its focus on each training instance.

翻译：本文旨在提供对超度参数模型效果的理解,例如深神经网络,记忆依赖实例的噪音标签。我们首先从样本分布的不同光谱中量化因记忆噪音事件造成的伤害。然后我们分析如何在实例一级通过一些流行的办法来学习噪音标签来减轻这种伤害。我们的分析揭示了对这些方法何时起作用的新理解,并为先前报告的经验性观察提供了理论依据。分析的一个重要方面是侧重于每个培训实例。