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

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Can you tell me Write a function to normalize embeddings in a One-Shot Learning framework to improve model performance on unseen classes.
Apr 4 in Generative AI by Nidhi
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1 answer to this question.

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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.
answered Apr 9 by nimantra

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