What is Contrastive Learning?

Detailed Deep Dive

Contrastive learning is a self-supervised learning paradigm where a model learns data representations by comparing similar and dissimilar samples. The training objective forces representations of positive pairs (e.g., two different views of the same image) to be close together in vector space, while pushing representations of negative pairs (different images) far apart, enabling the model to learn semantic features without labels.