Semi-Supervised Learning

Semi-supervised learning builds models from both labeled and unlabeled data.

Fully supervised learning needs a label on every example. Unsupervised learning uses none. Semi-supervised starts with a small labeled set and a much larger unlabeled pool, then uses both. Labeling is expensive. In medical imaging a radiologist may charge thousands of dollars to annotate one scan. Semi-supervised methods can approach fully supervised accuracy with a fraction of the labels.

The algorithm learns patterns from the labeled examples, then spreads that signal across unlabeled points by clustering similar inputs or assigning pseudo-labels. Speech recognition, fraud detection, and autonomous driving use this when raw sensor data is plentiful and verified labels are scarce. Teams train on the data they already collect, plus a modest set of checked examples.

Start with a small labeled set. Train a first model. Label unlabeled points that the model is confident about, or pull unlabeled points toward labeled clusters. Repeat. A scan that costs thousands of dollars to annotate can sit next to thousands of raw scans that cost nothing to store. Speech, fraud, and driving already have those raw streams.

Performance can approach a fully labeled system if the unlabeled pool matches the deployment distribution. If it does not, pseudo-labels spread the mismatch. Semi-supervised methods use a few labels plus many unlabeled examples. That is how teams cope when labeling is expensive.