siamese-networks-share-identical-weights

Summary

A Siamese Network processes two inputs through the exact same sub-model (same parameters, same layers), so it can't treat one input differently just because it appears on the left versus the right. This forces it to learn a general, position-independent way of measuring how similar or different two things are, which is what makes it useful for tasks like matching, deduplication, or anomaly detection.