A siamese network is a neural network architecture that consists of two or more identical subnetworks with shared weights.
Each subnetwork processes a different input, and their outputs are compared using a distance or similarity function, such as cosine similarity or Euclidean distance.
The network is trained to minimize the distance between similar inputs and maximize the distance between dissimilar ones. Siamese networks are commonly used in tasks like face recognition, sentence similarity, and signature verification, where comparing two inputs directly is more important than classifying them individually.