A Self-supervised learning (SSL) technique that teaches models to identify similar and dissimilar data points, pulling related samples (positive pairs) closer together in an embedding space while pushing unrelated samples (negative pairs) apart.
It is highly effective for learning meaningful representations from unlabeled data, commonly using data augmentation to create positive pairs (e.g., different crops of the same image).