  Leveraging weak or noisy supervision for building effective machine learning
models has long been an important research problem. Its importance has further
increased recently due to the growing need for large-scale datasets to train
deep learning models. Weak or noisy supervision could originate from multiple
sources including non-expert annotators or automatic labeling based on
heuristics or user interaction signals. There is an extensive amount of
previous work focusing on leveraging noisy labels. Most notably, recent work
has shown impressive gains by using a meta-learned instance re-weighting
approach where a meta-learning framework is used to assign instance weights to
noisy labels. In this paper, we extend this approach via posing the problem as
label correction problem within a meta-learning framework. We view the label
correction procedure as a meta-process and propose a new meta-learning based
framework termed MLC (Meta Label Correction) for learning with noisy labels.
Specifically, a label correction network is adopted as a meta-model to produce
corrected labels for noisy labels while the main model is trained to leverage
the corrected labeled. Both models are jointly trained by solving a bi-level
optimization problem. We run extensive experiments with different label noise
levels and types on both image recognition and text classification tasks. We
compare the reweighing and correction approaches showing that the correction
framing addresses some of the limitation of reweighting. We also show that the
proposed MLC approach achieves large improvements over previous methods in many
settings.
