Small sample learning, why is it called Few-Shot Learning, not Few-Data Learning?
Mudasir Zaheer
Few-shot learning is called so because it focuses on the challenge of learning and generalizing from a very small number of examples or "shots." While the term "Few-Data Learning" could be used to describe a similar concept, "Few-Shot Learning" specifically emphasizes the ability of a model to make accurate predictions or classifications with a limited number of instances or "shots" of data. This terminology highlights the emphasis on the model's capacity to generalize knowledge effectively from just a few examples, which is a key characteristic of this machine-learning paradigm.
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