Few-shot learning (FSL) is a problem setup in machine learning in which a model learns to perform a task, typically classification, from only a small number of labeled examples per class, rather than the large datasets required by conventional supervised learning. One-shot learning is the special case of the N-way K-shot framing in which K equals one, such that the model must generalize from exactly one example per class.
Few-shot learning (FSL) is a problem setup in machine learning in which a model learns to perform a task, typically classification, from only a small number of labeled examples per class, rather than the large datasets required by conventional supervised learning. One-shot learning is the special case of the N-way K-shot framing in which K equals one, such that the model must generalize from exactly one example per class.
Source: Wikipedia "Few-shot learning" · CC BY-SA 4.0
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