Few-shot learning aims to adapt models trained on the base dataset to novel tasks where the categories are not seen by the model before. This often leads to a relatively uniform distribution of feature values across channels on novel classes, posing challenges in determining channel importance for novel tasks. Standard few-shot learning methods employ geometric similarity metrics such as cosine similarity and negative Euclidean distance to gauge the semantic relatedness between two features. However, features with high geometric similarities may carry distinct semantics, especially in the context of few-shot learning. In this paper, we demonstrate that the importance ranking of feature channels is a more reliable indicator for few-shot learning than geometric similarity metrics. We observe that replacing the geometric similarity metric with Kendall's rank correlation only during inference is able to improve the performance of few-shot learning across a wide range of datasets with different domains. Furthermore, we propose a carefully designed differentiable loss for meta-training to address the non-differentiability issue of Kendall's rank correlation. Extensive experiments demonstrate that the proposed rank-correlation-based approach substantially enhances few-shot learning performance.

翻译：少样本学习旨在将基于基数据集训练的模型泛化至模型未见类别的新任务中。这通常导致新类别特征值在通道间呈现相对均匀的分布，为确定新任务中通道重要性带来挑战。标准少样本学习方法采用余弦相似度与负欧氏距离等几何相似度度量来评估两个特征间的语义相关性。然而，具有高几何相似度的特征可能承载不同语义，尤其在少样本学习场景中。本文证明，特征通道的重要性排序是比几何相似度度量更可靠的少样本学习指标。我们观察到，仅需在推理阶段用Kendall秩相关替代几何相似度度量，即可在跨领域数据集上显著提升少样本学习性能。此外，针对Kendall秩相关的不可微问题，我们提出一种精心设计的可微元训练损失函数。大量实验表明，所提出的基于秩相关的方法可显著增强少样本学习性能。