Localized Meta-Learning: A PAC-Bayes Analysis for Meta-Learning 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. Such meta-knowledge is often represented as a fixed distribution; this, however, may be too restrictive to capture various specific task information because the discriminative patterns in the data may change dramatically across tasks. In this work, we aim to equip the meta learner with the ability to model and produce task-specific meta-knowledge and, accordingly, present a localized meta-learning framework based on the PAC-Bayes theory. In particular, we propose a Local Coordinate Coding (LCC) based prior predictor that allows the meta learner to generate local meta-knowledge for specific tasks adaptively. We further develop a practical 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
