Siamese Network

A siamese network is a neural network architecture that feeds two inputs through identical, weight-sharing sub-networks and compares their outputs to measure similarity between them.

What It Is

Most machine learning models take one input and produce one output — a classifier reads an image and says “cat” or “dog.” But what if you need to answer a different question: “How similar are these two things?” That is exactly the problem siamese networks were designed to solve. Whether you are comparing two sentences for meaning, two signatures for authenticity, or two product photos for duplicates, you need an architecture built for comparison rather than classification.

A siamese network works like a pair of identical twins reading two separate documents. Both twins went to the same school, learned the same vocabulary, and developed the same reading habits — they are clones of each other. When each twin reads their assigned document, they produce a summary. You then compare those summaries to decide whether the documents discuss the same topic.

In technical terms, the architecture consists of two sub-networks that share the same weights and parameters. “Shared weights” means both sub-networks are literally the same neural network applied twice — once to each input. Each sub-network transforms its input into a fixed-size vector (an embedding), and a comparison function like cosine similarity measures the distance between those two vectors. A small distance means the inputs are similar; a large distance means they are different.

This architecture became particularly important for sentence-level comparisons. According to Reimers & Gurevych 2019, applying a siamese structure to BERT — the approach behind Sentence-BERT — reduced the time needed to compare ten thousand sentence pairs from roughly sixty-five hours to about five seconds. The key insight was that once each sentence has been converted into an embedding through the shared encoder, you can precompute and store those embeddings. New comparisons become simple vector math instead of running the full model again for every pair.

The training process teaches the shared network what “similar” and “different” mean for a specific task. During contrastive learning, the model sees pairs of inputs labeled as similar or dissimilar and adjusts its weights until similar inputs produce nearby embeddings and dissimilar inputs produce distant ones. This is why siamese networks are tightly connected to how Sentence Transformers produce sentence-level embeddings — the siamese structure is the engine that makes contrastive learning work at the sentence level.

How It’s Used in Practice

The most common place you will encounter siamese networks today is semantic search. When a search system needs to find documents that match the meaning of your query — not just documents containing the same keywords — it relies on a siamese-style encoder to convert both the query and every document into embeddings. The system then ranks documents by how close their embeddings are to the query embedding.

Sentence Transformers, the most widely adopted library for this task, uses siamese BERT-based architectures as its default approach. According to SBERT Docs, supported backbone models include BERT, RoBERTa, ALBERT, and ModernBERT, among others. You encode your corpus once, store the resulting vectors in an index, and compare new queries against that index in milliseconds. This same principle powers duplicate detection in customer support tickets, plagiarism checkers, and recommendation engines that suggest “similar items.”

Pro Tip: If you are building a semantic search prototype, encode your documents once and cache the embeddings. The expensive part is the initial encoding pass — after that, each new query only needs one forward pass through the encoder, and the comparison step is just vector math.

When to Use / When Not

Common Misconception

Myth: A siamese network contains two separate neural networks that learn independently. Reality: The two sub-networks are the same network with shared weights. Every weight update during training affects both sides equally because they are literally one set of parameters applied twice. This weight sharing is what forces the network to learn a general-purpose representation rather than memorizing input-specific patterns.

One Sentence to Remember

A siamese network turns the question “how similar are these two things?” into simple vector math by running both inputs through the same encoder and comparing the results — and that one idea is what makes fast, precomputed semantic search possible.

FAQ

Q: How is a siamese network different from a standard classifier? A: A classifier assigns labels to single inputs. A siamese network compares two inputs by producing an embedding for each and measuring the distance between them, so it answers “how similar” rather than “what category.”

Q: Why do both sub-networks share weights? A: Shared weights ensure both inputs are processed with the same learned representation. This makes the resulting embeddings directly comparable — if each side learned different features, distance measurements between their outputs would be meaningless.

Q: Can a siamese network handle more than two inputs at once? A: Yes. The same shared encoder can process any number of inputs independently. You encode an entire corpus into embeddings once, then compare a new query against all stored embeddings using vector similarity.

Sources

• Reimers & Gurevych 2019: Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks - foundational paper introducing siamese BERT architecture for sentence embeddings
• SBERT Docs: SentenceTransformers Documentation - official documentation for the Sentence Transformers library