Few-Shot Learning via Learning the Representation, Provably

international conference on learning representations, 2020.

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We study when and how much representation learning can help few-shot learning by drastically reducing sample complexity on the target task.

Abstract:

This paper studies few-shot learning via representation learning, where one uses $T$ source tasks with $n_1$ data per task to learn a representation in order to reduce the sample complexity of a target task for which there is only $n_2 (\ll n_1)$ data. Specifically, we focus on the setting where there exists a good \emph{common represen...More

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