Siamese Networks

Siamese Networks are a class of neural networks that contain two or more identical subnetworks, which share the same parameters and weights. These networks are particularly effective for tasks that involve comparing two inputs, such as image similarity or face verification. The architecture allows the model to learn a similarity function, which can measure how close or similar two inputs are to each other. Common use cases include facial recognition systems, signature verification, and one-shot learning tasks, where the model learns to recognize new classes from only a single example. By leveraging shared weights, Siamese Networks can generalize better across different input pairs, making them a powerful tool in various machine learning applications.