  In supervised machine learning, use of correct labels is extremely important
to ensure high accuracy. Unfortunately, most datasets contain corrupted labels.
Machine learning models trained on such datasets do not generalize well. Thus,
detecting their label errors can significantly increase their efficacy. We
propose a novel framework, called CTRL (Clustering TRaining Losses for label
error detection), to detect label errors in multi-class datasets. It detects
label errors in two steps based on the observation that models learn clean and
noisy labels in different ways. First, we train a neural network using the
noisy training dataset and obtain the loss curve for each sample. Then, we
apply clustering algorithms to the training losses to group samples into two
categories: cleanly-labeled and noisily-labeled. After label error detection,
we remove samples with noisy labels and retrain the model. Our experimental
results demonstrate state-of-the-art error detection accuracy on both image
(CIFAR-10 and CIFAR-100) and tabular datasets under simulated noise. We also
use a theoretical analysis to provide insights into why CTRL performs so well.
