Supervised Contrastive Learning

A supervised contrastive learning method proposed by Khosla et al. in 2020, extending self-supervised contrastive learning. It applies a loss function that pulls representations of same-class samples closer together while pushing different-class samples apart, forming compact and well-separated class clusters in feature space. It is suited for tasks with high intra-class variance or high inter-class similarity.