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In addition to similarity learning, **Siamese networks** are also powerful feature extractors. The shared weight networks learn to identify important features in the input data, and these features are then used to compute the similarity score. Think about it this way: the network learns to distill the essence of each input into a compact, informative representation. Because the subnetworks are identical, they're forced to learn the same features from different inputs. This approach is especially useful when you have limited labeled data because the shared weights help to regularize the learning process and prevent overfitting. This is another reason why they are a great choice when dealing with images, text, and other types of complex data.
