Definition
This method encourages the model to pull embeddings of positive pairs (similar items) closer together while pushing negative pairs (dissimilar items) apart in the latent space. It is widely used in computer vision and NLP to learn robust feature representations without extensive labeled data. By focusing on relative differences, contrastive learning improves generalization capabilities across various downstream tasks.
Summary
Contrastive learning is a self-supervised technique that trains models to distinguish between similar and dissimilar data pairs.
Key Concepts
- Embeddings
- Positive/Negative Pairs
- Loss Function
- Self-Supervision
Use Cases
- Image retrieval systems
- Semantic search engines
- Face recognition verification