Contrastive Learning for Defect Embeddings is the training of a representation model that maps defect images to a feature space where similar defects are close and dissimilar defects are far apart — creating meaningful defect representations without requiring class labels.

How Contrastive Learning Works for Defects

• Positive Pairs: Two augmented views of the same defect image are pulled together in embedding space.
• Negative Pairs: Views from different defects are pushed apart.
• Losses: InfoNCE, NT-Xent, or triplet loss enforces the embedding structure.
• Frameworks: SimCLR, MoCo, BYOL, DINO adapted for defect images.

Why It Matters

• No Labels Needed: Learns useful representations without class labels — purely self-supervised.
• Downstream Tasks: Contrastive embeddings transfer to classification, retrieval, and clustering tasks.
• Defect Retrieval: Find similar historical defects by nearest-neighbor search in embedding space.

Contrastive Learning is teaching the model defect similarity — learning to organize defect images by visual similarity without being told the categories.