Semi-Supervised Learning

A training approach that combines a small amount of labeled data with a large amount of unlabeled data. The labeled examples guide the learning signal while the unlabeled data helps the model build richer representations of the underlying structure. Semi-supervised learning is practical in many real-world settings where labeling everything is too costly, and is closely related to self-supervised learning in its motivation if not its mechanics.