A grounding on how contrastive learning builds embedding spaces where similar samples cluster and dissimilar ones separate — the mechanism behind CLIP-style models and modern retrieval/embedding pipelines that engineers rely on for semantic search and RAG.
The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart.
Contrastive learning can be applied to both supervised and unsupervised settings.
When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning .
Transcript
The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning .
