  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.
