How do large-scale unsupervised systems perform feature distillation?

They transfer knowledge from large models to smaller models using representation alignment without labels.

Unsupervised distillation aligns embeddings between teacher and student models using similarity-based losses rather than labeled supervision. This can include cosine similarity loss, KL divergence over soft embeddings, or contrastive alignment. It enables deployment of lightweight models while preserving semantic understanding learned from large-scale training.