Label noise is a phenomenon in which the annotation information in the dataset is inconsistent with the real situation, including annotation errors, missing labels, wrong labels, category confusion, etc. Label noise will mislead model training, leading to reduced model accuracy and poor generalization ability, which is one of the key problems affecting the effect of AI models.
