A Siamese network is a type of neural network architecture used primarily for tasks involving comparison between two inputs, such as similarity detection. It can be used to check if two images are of the same person (e.g., FaceNet), to determine if two signatures come from the same person, or to find the semantic similarity between two sentences or documents, etc. Siamese Networks are particularly useful for tasks like face recognition, signature verification, and object tracking, where the network learns to differentiate between similar and dissimilar instances. They are effective even with limited training data, making them suitable for applications like one-shot learning.
Technically, a Siamese Network is a type of neural network architecture that contains two or more identical sub-networks. These sub-networks have the same configuration, parameters, and weights, and they process two different input vectors to compute comparable output vectors. The goal is to learn a similarity function that can compare inputs and determine how similar they are.
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