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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.
