siamese networks

Siamese networks learn similarity by comparing pairs of examples, useful for verification and few-shot learning. **Architecture**: Twin networks with shared weights process two inputs, produce embeddings, compare embeddings with distance/similarity metric. **Training**: Contrastive loss - same-class pairs should be close, different-class pairs far. Triplet loss - anchor closer to positive than negative by margin. **Inference**: Compare query to each support example, classify by most similar or aggregate similarities. **Applications**: Face verification (same person?), signature verification, one-shot learning, duplicate detection, image similarity search. **Advantages**: No retraining for new classes, naturally handles open-set scenarios, learn meaningful similarity metric. **Architecture choices**: Shared weights (Siamese), different weights for different inputs, various embedding networks (CNNs, transformers). **Loss functions**: Contrastive loss (pairs), triplet loss (anchor, pos, neg), N-pair loss, InfoNCE. **Relationship to metric learning**: Siamese instantiates learned distance metric. **Modern use**: Foundation for contrastive learning, representation learning, still used for verification tasks.