Contrastive Learning
Contrastive Learning is a self-supervised training technique where a model learns to group similar inputs (positive pairs) close together in embedding space while pushing dissimilar inputs (negative pairs) far apart.
Frequently Asked Questions
How is contrastive learning used in multimodal AI?▼
It aligns images and their text captions by training the model to match the correct image-text pairs in a shared embedding space.
What is a positive vs. negative pair?▼
A positive pair consists of different representations of the same concept (e.g. cropped versions of the same photo). A negative pair contains unrelated concepts.
Quick Facts
- CategoryModel Training
- Key ApplicationContrastive Image-Text Pre-training (CLIP), sentence embeddings, and visual representations
Coverage Trend12 Weeks
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