In this paper, we explore contrastive learning for few-shot classification,
in which we propose to use it as an additional auxiliary training objective
acting as a data-dependent regularizer to promote more general and transferable
features. In particular, we present a novel attention-based spatial contrastive
objective to learn locally discriminative and class-agnostic features. As a
result, our approach overcomes some of the limitations of the cross-entropy
loss, such as its excessive discrimination towards seen classes, which reduces
the transferability of features to unseen classes. With extensive experiments,
we show that the proposed method outperforms state-of-the-art approaches,
confirming the importance of learning good and transferable embeddings for
few-shot learning.

本文探讨使用反差学习作为辅助训练目标来促进更通用和可转移的特征，在此基础上提出了一种基于注意力的空间反差目标来学习本地区分和类别不可知特征。通过大量实验证明了该方法优于最先进的方法，证实了学习良好、可转移的嵌入在 few-shot 学习中的重要性。