  Traditional methods for learning with the presence of noisy labels have
successfully handled datasets with artificially injected noise but still fall
short of adequately handling real-world noise. With the increasing use of
meta-learning in the diverse fields of machine learning, researchers leveraged
auxiliary small clean datasets to meta-correct the training labels.
Nonetheless, existing meta-label correction approaches are not fully exploiting
their potential. In this study, we propose an Enhanced Meta Label Correction
approach abbreviated as EMLC for the learning with noisy labels (LNL) problem.
We re-examine the meta-learning process and introduce faster and more accurate
meta-gradient derivations. We propose a novel teacher architecture tailored
explicitly to the LNL problem, equipped with novel training objectives. EMLC
outperforms prior approaches and achieves state-of-the-art results in all
standard benchmarks. Notably, EMLC enhances the previous art on the noisy
real-world dataset Clothing1M by $1.52\%$ while requiring $\times 0.5$ the time
per epoch and with much faster convergence of the meta-objective when compared
to the baseline approach.
