The goal of few-shot classification is to classify new categories with few
labeled examples within each class. Nowadays, the excellent performance in
handling few-shot classification problems is shown by metric-based
meta-learning methods. However, it is very hard for previous methods to
discriminate the fine-grained sub-categories in the embedding space without
fine-grained labels. This may lead to unsatisfactory generalization to
fine-grained subcategories, and thus affects model interpretation. To tackle
this problem, we introduce the contrastive loss into few-shot classification
for learning latent fine-grained structure in the embedding space. Furthermore,
to overcome the drawbacks of random image transformation used in current
contrastive learning in producing noisy and inaccurate image pairs (i.e.,
views), we develop a learning-to-learn algorithm to automatically generate
different views of the same image. Extensive experiments on standard few-shot
learning benchmarks demonstrate the superiority of our method.

通过引入对比损失和学习算法，本研究提升了少样本分类任务中的细分子类别鉴别能力，实现了在嵌入空间中的潜在细粒度结构学习，并通过在标准小样本学习基准测试中的广泛试验验证了该方法的优越性。