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) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence. Whereas numerous studies have developed these principles independently, here, we combine them, building on the assumption of a classification noise process to directly estimate the joint distribution between noisy (given) labels and uncorrupted (unknown) labels. This results in a generalized CL which is provably consistent and experimentally performant. We present sufficient conditions where CL exactly finds label errors, and show CL performance exceeding seven state-of-the-art approaches for learning with noisy labels on the CIFAR dataset. We also employ CL on ImageNet to quantify ontological class overlap (e.g. finding approximately 645 \emph{missile} images are mislabeled as their parent class \emph{projectile}), and moderately increase model accuracy (e.g. for ResNet) by cleaning data prior to training. These results are replicable using the open-source \texttt{cleanlab} release. Show Chinese abstract

Abstract: 学习存在于数据的背景下，但通常\emph{置信度}的概念主要关注模型预测，而非标签质量。 置信学习（CL）是一种替代方法，它转而通过识别数据集中的标签错误来专注于标签质量，基于的原则包括清理噪声数据、使用概率阈值计数以估计噪声以及对示例进行排名以便以信心训练。 尽管许多研究独立开发了这些原则，但在这里，我们结合了它们，并基于分类噪声过程的假设直接估计有噪声（已知）标签和未受污染（未知）标签之间的联合分布。 这导致了一种广义的CL，它在理论上是一致的，并且在实验中表现出色。 我们提出了CL精确找到标签错误的充分条件，并展示了CL在CIFAR数据集上处理有噪声标签的学习中超过七个最先进的方法的表现。 我们还在ImageNet上应用CL来量化本体类重叠（例如，发现大约645张\emph{导弹}图像被误标为其父类\emph{弹丸}），并通过在训练前清理数据适度提高了模型准确性（例如，对于ResNet）。 这些结果可以通过开源的\texttt{清洁实验室}版本来复制。