  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
degradation in generalization performance. To resolve this issue, we propose
MARVEL (MARgins Via Early Learning), where we track the goodness of "fit" for
every instance by maintaining an epoch-history of its classification margins.
Based on consecutive negative margins, we discard suspected noisy instances by
zeroing out their weights. In addition, MARVEL+ upweights arduous instances
enabling the network to learn a more nuanced representation of the
classification boundary. Experimental results on benchmark datasets with
synthetic label noise show that MARVEL outperforms other baselines consistently
across different noise levels, with a significantly larger margin under
asymmetric noise.
