Confident Learning: Estimating Uncertainty in Dataset Labels

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 the 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 exceeds seven state-of-the-art approaches for learning with noisy labels on the CIFAR dataset. The CL framework is \emph{not} coupled to a specific data modality or model: we use CL to find errors in the presumed error-free MNIST dataset and improve sentiment classification on text data in Amazon Reviews. 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.