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arXiv · 2108.04055

The Role of Global Labels in Few-Shot Classification and How to Infer Them

Ruohan Wang·Massimiliano Pontil·Carlo Ciliberto
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Abstract

Few-shot learning is a central problem in meta-learning, where learners must quickly adapt to new tasks given limited training data. Recently, feature pre-training has become a ubiquitous component in state-of-the-art meta-learning methods and is shown to provide significant performance improvement. However, there is limited theoretical understanding of the connection between pre-training and meta-learning. Further, pre-training requires global labels shared across tasks, which may be unavailable in practice. In this paper, we show why exploiting pre-training is theoretically advantageous for meta-learning, and in particular the critical role of global labels. This motivates us to propose Meta Label Learning (MeLa), a novel meta-learning framework that automatically infers global labels to obtains robust few-shot models. Empirically, we demonstrate that MeLa is competitive with existing methods and provide extensive ablation experiments to highlight its key properties.

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Ruohan Wang, Massimiliano Pontil, Carlo Ciliberto. 2021-08-09. The Role of Global Labels in Few-Shot Classification and How to Infer Them. https://arxiv.org/abs/2108.04055

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