A Siamese neural network is a type of neural network architecture that is commonly used for tasks such as image recognition, face verification, and similarity matching. It is designed to learn and compare similarity or dissimilarity between pairs of inputs. The architecture consists of two or more identical subnetworks that share the same weights and parameters. Each subnetwork takes in one input and processes it through a series of layers to extract relevant features. The outputs of the subnetworks are then compared using a distance metric, such as Euclidean distance or cosine similarity, to determine the similarity between the inputs. Siamese neural networks are particularly useful when there is limited labeled data available for training. By learning to compare pairs of inputs, the network can generalize well to unseen examples and make accurate similarity judgments. Some applications of Siamese neural networks include face recognition, signature verification, plagiarism detection, and recommendation systems.
Обсуждают сегодня