This repository aggregates resources for the machine learning subfield of learning with noisy labels, where training data contains mislabeled or unreliable annotations. It catalogs peer-reviewed papers from venues like NeurIPS, ICML, and ICLR spanning over 15 years of research, along with associated code repositories. The list covers methods for bias mitigation, robust loss functions, crowdsourcing, and semi-supervised approaches to handle label uncertainty in deep learning.