Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Leanring Beyond Global Prior
Meta-learning methods learn the meta-knowledge among various training tasks and aim to promote the learning of new tasks under the task similarity assumption. However, such meta-knowledge is often represented as a fixed distribution, which is too restrictive to capture various specific task information. In this work, we present a localized meta-learning framework based on PAC-Bayes theory. In particular, we propose a LCC-based prior predictor that allows the meta learner adaptively generate local meta-knowledge for specific task. We further develop a pratical algorithm with deep neural network based on the bound. Empirical results on real-world datasets demonstrate the efficacy of the proposed method.
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CIFAR-100
Caltech-256
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