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

Title: Confident Learning: 估计数据集标签中的不确定性

Abstract: Learning exists in the context of data, yet notions of \emph{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 across reasonable conditions, and experimentally performant on ImageNet and CIFAR, outperforming seven recent approaches 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\emph{置信度}的概念通常侧重于模型预测，而不是标签质量。 自信学习（CL）作为一种方法，在清理噪声数据、通过计数估计噪声以及根据示例排序以自信训练的基础上，被提出用于表征、识别和处理数据集中的噪声标签。 在这里，我们基于分类噪声过程的假设，将CL推广，以直接估计噪声（已知）标签与未受污染（未知）标签之间的联合分布。 这一推广后的CL作为\texttt{清洁实验室}开源，并在合理条件下具有理论一致性，同时在ImageNet和CIFAR上实验表现良好，在非均匀标签噪声情况下优于七种最新方法。 此外，\texttt{清洁实验室}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）。