How Important is the Train-Validation Split in Meta-Learning?

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Other Links: arxiv.org

Abstract:

Meta-learning aims to perform fast adaptation on a new task through learning a "prior" from multiple existing tasks. A common practice in meta-learning is to perform a train-validation split where the prior adapts to the task on one split of the data, and the resulting predictor is evaluated on another split. Despite its prevalence, the...More

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