Glossario IA

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Few-Shot Learning (FSL)

Machine learning paradigm where a model learns to recognize new classes from a very small number of training examples, typically between 1 and 5 samples per class.

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N-way K-shot Learning

Formalization of the few-shot problem where N represents the number of classes to discriminate and K the number of available examples per class for learning.

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Feature Reweighting

Few-shot technique that dynamically adjusts the importance of extracted features based on support examples to better adapt to new classes.