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Okay, let's break down the architecture a bit further. The core components of a **Siamese network** are: identical subnetworks. Each subnetwork typically consists of several layers of convolutional or fully connected layers, depending on the nature of the input data. They share the same weights, ensuring that they learn the same features. Input pairs. The network takes two (or more) input samples and passes them through their respective subnetworks. Feature vectors. Each subnetwork generates a feature vector that represents the input data. Distance or similarity function. This function compares the feature vectors and produces a measure of similarity or dissimilarity. Loss function. The loss function guides the training process by penalizing incorrect similarity/dissimilarity predictions.