What is Siamese Network

How It Works

A Siamese network processes two inputs through identical encoder subnetworks that share the same weights. Each input is mapped to an embedding vector, and a distance metric (cosine similarity or Euclidean distance) measures how similar the two embeddings are. The network is trained so that similar pairs produce close embeddings and dissimilar pairs produce distant ones.

Technical Details

Siamese networks are trained using contrastive loss or triplet loss on pairs or triplets of examples. The shared-weight architecture ensures consistent embedding computation regardless of input order. At inference time, embeddings can be pre-computed and cached for one side, making similarity search efficient. Siamese networks generalize well to unseen classes, making them ideal for few-shot and zero-shot scenarios.

Best Practices

• Balance positive and negative pairs during training to prevent the model from learning trivial solutions
• Use online hard example mining to focus on the most challenging pairs
• Pre-compute reference embeddings for known items to speed up inference
• Choose the distance metric based on your embedding normalization strategy

Common Pitfalls

• Training with too-easy negatives that lead to embeddings that collapse or lack discrimination
• Not sharing weights between the twin networks, which defeats the architecture's purpose
• Using insufficient training pairs, leading to poor generalization
• Ignoring class imbalance in the pair construction process

Advanced Tips

• Extend to triplet networks with anchor, positive, and negative inputs for stronger supervision
• Use cross-modal Siamese architectures to learn joint embeddings for images and text
• Apply metric learning losses (angular, circle loss) for better gradient properties
• Combine Siamese networks with attention mechanisms for fine-grained similarity