Deep learning models need a lot of labeled data, which is costly and time-consuming to collect. A method that learns a common knowledge (meta-knowledge) from similar tasks to train new tasks efficiently with fewer data is effective. We propose "Feature-adaptive Re-MAML" (FARe-MAML), a novel method for acquiring the most optimal learning method, taking into account the features of the new training data.
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Sunata et al. (2024) studied this question.
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