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Not all data in a typical training set help with generalization; some samplescan be overly ambiguous or outrightly mislabeled. This paper introduces a newmethod to identify such samples and mitigate their impact when training neuralnetworks. At the heart of our algorithm is the Area Under the Margin (AUM)statistic, which exploits differences in the training dynamics of clean andmislabeled samples. A simple procedure - adding an extra class populated withpurposefully mislabeled threshold samples - learns a AUM upper bound thatisolates mislabeled data. This approach consistently improves upon prior workon synthetic and real-world datasets. On the WebVision50 classification taskour method removes 17% of training data, yielding a 1.6% (absolute) improvementin test error. On CIFAR100 removing 13% of the data leads to a 1.2% drop inerror.

Identifying Mislabeled Data using the Area Under the Margin Ranking