Methods › General › Label Correction › Label Quality Model
Label Quality Model
Introduced by Keren Gu et al. in An Instance-Dependent Simulation Framework for Learning with Label Noise
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Label Quality Model is an intermediate supervised task aimed at predicting the clean labels from noisy labels by leveraging rater features and a paired subset for supervision. The LQM technique assumes the existence of rater features and a subset of training data with both noisy and clean labels, which we call paired-subset. In real world scenarios, some level of label noise may be unavoidable. The LQM approach still works as long as the clean(er) label is less noisy than a label from a rater that is randomly selected from the pool, e.g., clean labels can be from either expert raters or aggregation of multiple raters. LQM is trained on the paired-subset using rater features and noisy label as input, and inferred on the entire training corpus. The output of LQM is used during model training as a more accurate alternative to the noisy labels.
Papers archive 2025-07-28
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An Instance-Dependent Simulation Framework for Learning with Label Noise 23 Jul 2021 · 1 repository · arXiv:2107.11413
Tasks archive 2025-07-28
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| Task | Papers |
|---|---|
| Learning with noisy labels | 1 |
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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