How does unsupervised learning help with noisy labels?
hardAnswer
- (1) Cluster the data unsupervisedly; check consistency of labels within clusters — outliers likely mislabeled.
- (2) Confidence learning (Northcutt et al., cleanlab): use self-consistency + probabilistic label pruning.
- (3) Small-loss trick during training (Co-teaching): assume noisy samples have larger loss; ignore top-loss fraction.
- (4) Semi-supervised: leverage unlabeled or auto-labeled data.
- Foundation of modern label cleaning at scale.
Check yourself — multiple choice
- Random
- Cluster consistency check + cleanlab confidence learning + small-loss Co-teaching + semi-supervised leverage — modern label cleaning at scale
- Same as supervised
- Not real
Noisy labels: cluster consistency + cleanlab + Co-teaching small-loss + semi-sup.
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Practise Unsupervised Learning
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