CVPR 2023 - Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings

In this episode we discuss Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings by Daniel J. Trosten, Rwiddhi Chakraborty, Sigurd Løkse, Kristoffer Knutsen Wickstrøm, Robert Jenssen, Michael C. Kampffmeyer. This paper proposes two approaches to address the hubness problem in distance-based classification in transductive few-shot learning. The authors prove that uniform distribution of representations on the hypersphere can eliminate hubness and the proposed approaches optimize a tradeoff between uniformity and local similarity preservation, reducing hubness while retaining class structure. Experiment results show that the proposed methods significantly improve transductive few-shot learning accuracy for a variety of classifiers.