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Poster
The Role of Global Labels in Few-Shot Classification and How to Infer Them
Ruohan Wang · Massimiliano Pontil · Carlo Ciliberto

Tue Dec 07 04:30 PM -- 06:00 PM (PST) @
in Poster Session 2 »

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.

Keywords: Machine Learning · Meta Learning · Few Shot Learning

Author Information

Ruohan Wang (Institute for Infocomm Research)
Massimiliano Pontil (IIT & UCL)
Carlo Ciliberto (University College London)

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