What is a Siamese Network and how is it used in one-shot learning

Question

With the help of proper code explanation can you tell me What is a Siamese Network, and how is it used in one-shot learning?

Answer 1

A Siamese Network is a neural architecture with twin networks sharing weights, used in one-shot learning to compute similarity between two inputs by learning a distance metric, commonly applied in face recognition, signature verification, and image matching.

Here is the code snippet you can refer to:

In the above code we are using the following key approaches:

• Shared Feature Extraction Using a Base CNN:

  • Both inputs pass through the same CNN, ensuring the same feature space.

• Distance Calculation Using an L1 Distance Layer:

  • Computes absolute difference between feature vectors to measure similarity.

• Binary Classification (Similar vs. Different):

  • Final sigmoid layer predicts if the two inputs belong to the same class.

• One-Shot Learning Capability:

  • Learns a distance metric, allowing recognition with very few training samples.

Hence, Siamese Networks excel in one-shot learning by learning similarity metrics, enabling recognition tasks with minimal labeled data and high generalization ability