In this paper, we look at the problem of few-shot classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples. Recent methods use adaptation networks for aligning their features to new domains or select the relevant features from multiple domain-specific feature extractors. In this work, we propose to learn a single set of universal deep representations by distilling knowledge of multiple separately trained networks after co-aligning their features with the help of adapters and centered kernel alignment. We show that the universal representations can be further refined for previously unseen domains by an efficient adaptation step in a similar spirit to distance learning methods. We rigorously evaluate our model in the recent Meta-Dataset benchmark and demonstrate that it significantly outperforms the previous methods while being more efficient. Our code will be available at https://github.com/VICO-UoE/URL.

本文介绍了通过适配器和居中核对齐等方法，利用多个分别训练的网络的知识来同时学习多个领域上通用的深度表示，通过距离学习方法来有效适应之前未见过的领域，并在 Meta-Dataset 基准测试中取得显著的性能提升。

多个领域的少样本分类通用表示学习