Meta-learning has been the most common framework for few-shot learning in
recent years. It learns the model from collections of few-shot classification
tasks, which is believed to have a key advantage of making the training
objective consistent with the testing objective. However, some recent works
report that by training for whole-classification, i.e. classification on the
whole label-set, it can get comparable or even better embedding than many
meta-learning algorithms. The edge between these two lines of works has yet
been underexplored, and the effectiveness of meta-learning in few-shot learning
remains unclear. In this paper, we explore a simple process: meta-learning over
a whole-classification pre-trained model on its evaluation metric. We observe
this simple method achieves competitive performance to state-of-the-art methods
on standard benchmarks. Our further analysis shed some light on understanding
the trade-offs between the meta-learning objective and the whole-classification
objective in few-shot learning.

本文探讨了在整个标签集上进行全分类的简单方法，并通过此方法在多个基准测试中取得了与现有技术相当的表现。同时，本文还对元学习与全分类目标在少样本学习中的权衡进行了深入分析。