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