Few-Shot Learning

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Few-shot learning is used when a system needs to learn something new from only a few examples. This often happens when collecting labeled data takes time or requires expert knowledge, such as in medical or technical fields. Instead of being trained on large datasets, the model must figure out what matters from very limited information.

The main difficulty is learning the right pattern without guessing based on noise. With so few examples, a model can easily focus on details that aren’t actually important. To handle this, few-shot approaches rely on knowledge the model learned earlier, for example, during large-scale pretraining or from related tasks. Performance is usually tested by showing the model a small set of examples and checking how well it applies that knowledge to new cases.

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