Popular approaches for few-shot classification consist of first learning a
generic data representation based on a large annotated dataset, before adapting
the representation to new classes given only a few labeled samples. In this
work, we propose a new strategy based on feature selection, which is both
simpler and more effective than previous feature adaptation approaches. First,
we obtain a multi-domain representation by training a set of semantically
different feature extractors. Then, given a few-shot learning task, we use our
multi-domain feature bank to automatically select the most relevant
representations. We show that a simple non-parametric classifier built on top
of such features produces high accuracy and generalizes to domains never seen
during training, which leads to state-of-the-art results on MetaDataset and
improved accuracy on mini-ImageNet.

本篇论文提出了一种基于特征选择的新策略，可以更加简单和有效地应对 few-shot 分类问题。该方法首先训练多个具有不同语义的特征提取器，然后根据特定任务的需求自动选择最相关的特征表示，并在此基础上构建简单的非参数分类器，可以在 MetaDataset 数据集上实现最新的结果及 mini-ImageNet 上实现进一步准确度的提高