论文标题
几次分类的空间对比学习
Spatial Contrastive Learning for Few-Shot Classification
论文作者
论文摘要
在本文中，我们探讨了几次分类的对比度学习，在其中我们建议将其用作额外的辅助培训目标，以作为数据依赖性正规化器，以促进更通用和可转移的功能。特别是，我们提出了一个新型的基于注意力的空间对比目标，以学习本地歧视性和类不足的特征。结果，我们的方法克服了交叉渗透损失的某些局限性，例如它对看到的类别的过度歧视，从而降低了特征向看不见的类别的可传递性。通过广泛的实验，我们表明所提出的方法的表现优于最先进的方法，从而确认学习良好和可转移的嵌入对于几次学习的重要性。
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.
