Learning Paradigms · Established · Advanced

Semi-Supervised Learning

Learning from a small labelled set combined with a much larger pool of unlabelled examples.

What Semi-Supervised Learning is

Semi-supervised learning assumes unlabelled data still carries information about the shape of the input distribution, so it can sharpen decision boundaries that a few labels alone would place badly.

How it works

Techniques include pseudo-labelling the confident predictions, consistency regularisation that forces stable predictions under augmentation, and pretraining on unlabelled data before fine-tuning on the labelled subset.

Why it matters

It matches the economics of most organisations: plenty of raw data, very few annotated examples, and a limited budget for expert labelling.

Common uses

→Medical imaging with scarce expert labels
→Speech recognition for low-resource languages
→Content moderation bootstrapping

Strengths

✓Big accuracy gains from few labels
✓Cheaper than full annotation

Watch for

✓Pseudo-labels can reinforce early mistakes

Continue exploring

Supervised Learning Self-Supervised Learning Active Learning Data Labeling