N-Shot Learning: Learning More with Less Data

To approach an issue as complex as this one, we need to first define it clearly. In the N-shot learning field, we have n labeled examples of each K classes, i.e. N∗K total examples which we call support set S. We also have to classify Query Set Q, where each example lies in one of the K classes. N-shot learning has three major sub-fields: zero-shot learning, one-shot learning, and few-shot learning, which each deserve individual attention.

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