A Siamese network is a type of neural network architecture primarily designed for tasks involving similarity comparison between pairs of inputs. In essence, it consists of two or more identical subnetworks that share the same weights and parameters. These subnetworks take separate inputs and produce their respective outputs, which are then combined to evaluate the degree of similarity or dissimilarity between the inputs. This architecture is particularly effective in applications such as image recognition, facial verification, and signature verification, where determining how alike or different two instances are is critical.
At the core of a Siamese network is the idea of a shared representation. Because both subnetworks share the same weights, they learn to extract similar features from their respective inputs. For instance, in a facial recognition task, one subnetwork might process an image of a person’s face, while the other processes a different image. The outputs of these networks can be fed into a distance-based loss function, like contrastive loss or triplet loss, which helps the model learn to minimize the distance between similar pairs and maximize the distance between dissimilar pairs. This approach encourages the network to learn a useful embedding space where similar items are closer together, while different items are farther apart.
Siamese networks are particularly advantageous when the available data for training is limited. Instead of needing a large labeled dataset for direct classification tasks, they can learn useful feature representations from pairs of samples, even if those samples belong to different classes. In practical terms, this means you can train the model on a relatively small number of labeled pairs, which is often easier. For instance, in signature verification, a Siamese network can compare a user's signature against known samples to determine authenticity, reducing the need for large datasets of signed images while still maintaining good performance in distinguishing between real and forged signatures.