What is contrastive loss and why is it important?

Contrastive loss learns representations by pulling similar samples closer and pushing dissimilar ones apart.

Contrastive learning defines a cost function based on similarity relationships rather than explicit labels. It uses positive and negative pairs to shape embedding spaces. This approach is fundamental in self-supervised learning and representation learning.