One-Shot Learning With Siamese Network

An intuitive explanation of Siamese Network

You have a new account holder in your bank and would like to set his signature for verification. You have only one sample signature from the member. How can you use a neural network to perform the signature verification?

To find the answer, read on…

Siamese network inspired by Siamese twins has a unique architecture to naturally rank similarity or dissimilarity between inputs.

Key features of Siamese Network

• Siamese network takes two different inputs passed through two similar subnetworks with the same architecture, parameters, and weights.
• The two subnetworks are a mirror image of each other, just like the Siamese twins. Hence, any change to any subnetworks architecture, parameter, or weights is also applied to the other subnetwork.
• The two subnetwork outputs an encoding to calculate the difference between the two inputs.
• The Siamese network's objective is to classify if the two inputs are the same or different using the Similarity score. The Similarity score can be calculated using Binary cross-entropy, Contrastive function, or Triplet loss, which are techniques for the general distance metric learning approach.
• Siamese network is a one-shot classifier that uses…