Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are prone to suffer from the hubness problem, where a few points (hubs) occur frequently in multiple nearest neighbour lists of other points. Hubness negatively impacts distance-based classification when hubs from one class appear often among the nearest neighbors of points from another class, degrading the classifier's performance. To address the hubness problem in FSL, we first prove that hubness can be eliminated by distributing representations uniformly on the hypersphere. We then propose two new approaches to embed representations on the hypersphere, which we prove optimize a tradeoff between uniformity and local similarity preservation -- reducing hubness while retaining class structure. Our experiments show that the proposed methods reduce hubness, and significantly improves transductive FSL accuracy for a wide range of classifiers.

翻译：基于距离的分类在直推式少样本学习(FSL)中经常使用。然而，由于图像表示的高维性，FSL分类器易受中枢问题影响，即个别点（中枢）频繁出现在其他点的多个最近邻列表中。当一个类别的中枢频繁出现在另一类别点的最近邻中时，中枢性会损害基于距离的分类性能，降低分类器的表现。针对FSL中的中枢问题，我们首先证明通过将表示均匀分布在超球面上可以消除中枢性。然后我们提出两种新的方法将表示嵌入到超球面上，并证明这两种方法优化了均匀性与局部相似性保持之间的权衡——既能降低中枢性又能保留类别结构。实验表明，所提出的方法降低了中枢性，并显著提升了多种分类器在直推式FSL任务中的准确率。