Few-Shot Learning
A machine learning paradigm where a model can learn a new task from only a few training examples, leveraging prior knowledge from pre-training.
In plain language
An AI that can learn a new task from just a handful of examples. Instead of needing thousands of examples, you show it 3-5 and it gets the idea, like a quick learner on day one.
Why this matters
Few-shot learning reduces the data collection and labelling burden for custom AI applications. This has implementation value for organisations that need to adapt foundation models to domain-specific tasks quickly and cost-effectively.
Relevance
ImplementationFew-shot learning accelerates time-to-value for custom AI applications by reducing dependency on large labelled datasets.
Related terms
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