A Siamese network is a deep learning architecture designed to compare two inputs by processing them through identical neural networks and measuring their similarity. It consists of two or more subnetworks (often called “twins”) that share the same parameters and weights, ensuring both inputs are transformed using the same feature extraction logic. The network outputs embeddings (numeric representations) for each input, and a similarity score is computed between them—for example, using cosine similarity or Euclidean distance. This approach is particularly useful for tasks where the relationship between pairs of data points matters more than individual classifications, such as face verification, signature matching, or detecting duplicate text.
Training a Siamese network typically involves pairs or triplets of data. For example, in face recognition, the network might receive two images: a “positive” pair (same person) and a “negative” pair (different people). The goal is to minimize the distance between embeddings of similar pairs while maximizing it for dissimilar ones. Loss functions like contrastive loss or triplet loss enforce this behavior. Triplet loss, for instance, uses an anchor, a positive example (same class as anchor), and a negative example (different class), adjusting weights so the anchor is closer to the positive than the negative by a defined margin. This setup allows the network to learn meaningful features without requiring labeled class data for every possible input, making it efficient for scenarios with limited labeled data.
Developers often use Siamese networks in scenarios requiring one-shot or few-shot learning. For instance, in document deduplication, the network can compare two text embeddings to detect duplicates without retraining on every new document. In object tracking, it can match target features across video frames. A practical advantage is the ability to precompute embeddings for frequently used data (e.g., a user’s face), reducing inference time. Implementing a Siamese network in frameworks like PyTorch or TensorFlow involves creating twin subnetworks with shared layers, then combining them with a custom loss function. For example, in PyTorch, you might define a single neural network module and pass both inputs through it, then compute similarity scores. This design avoids redundant code and ensures consistent feature extraction for both inputs, making it scalable and efficient for comparison tasks.