Paper link: https://arxiv.org/abs/1911.00068
Abstract: Learning exists in the context of data, yet notions of 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 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. 𝚌𝚕𝚎𝚊𝚗𝚕𝚊𝚋 also quantifies ontological class overlap, and can increase model accuracy (e.g. ResNet) by providing clean data for training.
The blog post further elaborates on the released paper, and it discusses an emerging, principled framework to identify label errors, characterize label noise, and learn with noisy labels known as confident learning (CL), open-sourced as the
cleanlab Python package.
Unlike most machine learning approaches, confident learning requires no hyperparameters. We use cross-validation to obtain predicted probabilities out-of-sample. Confident learning features a number of other benefits. CL
cleanlabPython package for characterizing, finding, and learning with label errors.
The theoretical and experimental results emphasize the practical nature of confident learning, e.g. identifying numerous label issues in ImageNet and CIFAR and improving standard ResNet performance by training on a cleaned dataset. Confident learning motivates the need for further understanding of uncertainty estimation in dataset labels, methods to clean training and test sets, and approaches to identify ontological and label issues in datasets.