Write a function to normalize embeddings in a One-Shot Learning framework to improve model performance on unseen classes

Question

Can you tell me Write a function to normalize embeddings in a One-Shot Learning framework to improve model performance on unseen classes.

Answer 1

Normalizing embeddings ensures uniform vector scales, improving cosine similarity-based matching for unseen classes in One-Shot Learning.

Here is the code snippet you can refer to:

In the above code, we are using the following key points:

• Uses L2 normalization to scale vectors to unit length.

• Handles edge cases (e.g., zero vectors) safely using epsilon.

• Enhances similarity comparison for matching queries and support samples.

Hence, normalization aligns embedding magnitudes, enabling reliable similarity comparisons in One-Shot Learning tasks.