What is contrastive learning in deep representation learning?

Contrastive learning trains models by distinguishing similar and dissimilar pairs.

It uses positive pairs (augmented versions of same sample) and negative pairs (different samples). Loss functions like InfoNCE maximize agreement between positives while separating negatives in embedding space. It is widely used in SimCLR, MoCo, and CLIP architectures.