An Introduction to Confident Learning: Finding and Learning with Label Errors in Datasets

This post overviews the paper Confident Learning: Estimating Uncertainty in Dataset Labels authored by Curtis G. Northcutt, Lu Jiang, and Isaac L. Chuang. If you've ever used datasets like CIFAR, MNIST, ImageNet, or IMDB, you likely assumed the class labels are correct. Why? Principled approaches for characterizing and finding label errors in massive datasets is challenging and solutions are limited. Surprise: there are likely at least 100,000 label issues in ImageNet. In this post, I discuss 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.