Siamese Network
A Siamese network is a neural network architecture consisting of two (or more) identical subnetworks that share the same weights and parameters. Instead of classifying inputs directly, it learns to map them into a feature space where similarity can be measured.
Background
Originally developed for handwritten signature verification, Siamese networks are widely used today in computer vision and natural language processing. The core idea is to encode inputs into embeddings and then compare them using a distance metric (e.g., Euclidean or cosine similarity).
Applications
- Face recognition: verifying whether two images belong to the same person.
- Duplicate detection: identifying near-duplicate images or text.
- Information retrieval: matching user queries with relevant documents.
- Biometric verification: fingerprints, iris scans, voice patterns.
Strengths and challenges
- ✅ Works well in few-shot and one-shot learning scenarios.
- ✅ Generalizes to unseen classes.
- ❌ Requires careful choice of similarity function.
- ❌ Computationally expensive when scaling to large datasets.
Siamese networks are particularly powerful because they shift the problem from classification to comparison. Instead of training a model to recognize every possible class directly—which becomes impractical with thousands of identities or limited samples—they learn a general notion of similarity. This is why they are central in metric learning.
A key innovation is the use of contrastive loss or triplet loss, which trains the network to minimize distances for similar pairs while maximizing them for dissimilar pairs. This approach has become standard in face recognition benchmarks and is also applied to text embeddings, where semantically similar sentences are pulled closer in vector space.
In practice, scalability remains an issue: comparing embeddings pairwise across millions of items can be computationally expensive. Techniques like approximate nearest neighbor search (e.g., FAISS) are often paired with Siamese networks to make retrieval feasible at scale. Their legacy continues to inspire modern architectures like Siamese Transformers for sentence similarity and cross-modal tasks.
📚 Further Reading
- Bromley, J., et al. (1994). Signature Verification using a Siamese Time Delay Neural Network.
- Koch, G. (2015). Siamese Neural Networks for One-shot Image Recognition.
