Principal subspaces of classifier weights are largely preserved under moderate label inaccuracy, which explains why models still learn from noisy labels.
Leveraging latent label distributions for partial label learning
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Why Can Accurate Models Be Learned from Inaccurate Annotations?
Principal subspaces of classifier weights are largely preserved under moderate label inaccuracy, which explains why models still learn from noisy labels.