  A deep neural network trained on noisy labels is known to quickly lose its
power to discriminate clean instances from noisy ones. After the early learning
phase has ended, the network memorizes the noisy instances, which leads to a
significant degradation in its generalization performance. To resolve this
issue, we propose MARVEL (MARgins Via Early Learning), a new robust learning
method where the memorization of the noisy instances is curbed. We propose a
new test statistic that tracks the goodness of "fit" of every instance based on
the epoch-history of its classification margins. If its classification margin
is small in a sequence of consecutive learning epochs, that instance is
declared noisy and the network abandons learning on it. Consequently, the
network first flags a possibly noisy instance, and then waits to see if
learning on that instance can be improved and if not, the network learns with
confidence that this instance can be safely abandoned. We also propose MARVEL+,
where arduous instances can be upweighted, enabling the network to focus and
improve its learning on them and consequently its generalization. Experimental
results on benchmark datasets with synthetic label noise and real-world
datasets show that MARVEL outperforms other baselines consistently across
different noise levels, with a significantly larger margin under asymmetric
noise.
