Countering Noisy Labels By Learning From Auxiliary Clean Labels

Understand

We consider the learning from noisy labels (NL) problem which emerges in many real-world applications.

• In addition to the widely-studied synthetic noise in the NL literature, we also consider the pseudo labels in semi-supervised learning (Semi-SL) as a special case of NL.
• For both types of noise, we argue that the generalization performance of existing methods is highly coupled with the quality of noisy labels.
• Therefore, we counter the problem from a novel and unified perspective: learning from the auxiliary clean labels.