Learning to Combat Noisy Labels via Classification Margins

Understand

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.