Title: Confident Learning: Estimating Uncertainty in Dataset Labels Show Chinese title

Title: 置信学习：估计数据集标签中的不确定性

Abstract: Learning exists in the context of data, yet notions of $\textit{confidence}$ typically focus on model predictions, not label quality. Confident learning (CL) has emerged as an approach for characterizing, identifying, and learning with noisy labels in datasets, based on the principles of pruning noisy data, counting to estimate noise, and ranking examples to train with confidence. Here, we generalize CL, building on the assumption of a classification noise process, to directly estimate the joint distribution between noisy (given) labels and uncorrupted (unknown) labels. This generalized CL, open-sourced as $\texttt{cleanlab}$, is provably consistent under reasonable conditions, and experimentally performant on ImageNet and CIFAR, outperforming recent approaches, e.g. MentorNet, by $30\%$ or more, when label noise is non-uniform. $\texttt{cleanlab}$ also quantifies ontological class overlap, and can increase model accuracy (e.g. ResNet) by providing clean data for training. Show Chinese abstract

Abstract: 学习存在于数据的背景下，然而关于$\textit{confidence}$的概念通常关注于模型预测，而不是标签质量。 自信学习（CL）作为一种方法，在数据集中基于清理噪声数据、计数估计噪声以及对示例进行排序以自信地训练的原则上，已经出现用于表征、识别和学习带有噪声标签的数据集。 在这里，我们基于分类噪声过程的假设，推广了CL，直接估计有噪声（给定）标签与未被污染（未知）标签之间的联合分布。 这种广义的CL，开源为$\texttt{cleanlab}$，在合理条件下具有理论一致性，并且在ImageNet和CIFAR上的实验表现优于最近的方法，例如当标签噪声非均匀时，比MentorNet高出$30\%$或更多。 $\texttt{cleanlab}$还量化了本体类别的重叠，并可以通过提供干净的数据来提高模型准确性（例如ResNet）。