Contrastive Learning

Understand Contrastive Learning with Positive and Negative Pairs

An embedding model turns an input into a vector so that useful relationships can be measured numerically. The difficult part is not producing vectors. It is teaching the geometry of the vector space: which inputs should be close, which should be far apart, and what “similar” should mean for the application. Contrastive learning provides a practical answer. Instead of training only from a class label such as billing or technical support, it trains from relationships between examples. A positive pair contains examples that should have similar representations. A negative pair contains examples that should not.