How do self-supervised learning models replace traditional unsupervised learning?

Self-supervised learning creates labels from data itself to learn representations.

Instead of purely unlabeled clustering, SSL generates surrogate tasks such as predicting masked tokens or contrasting augmented views. Models like SimCLR, MoCo, and BERT-style transformers learn representations that transfer well to downstream tasks. This bridges the gap between unsupervised and supervised learning performance.