  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.
