Hierarchical priors for Hyperspherical Prototypical Networks
Samuele Fonio, Lorenzo Paletto, Mattia Cerrato, Dino Ienco, Roberto Esposito · 2023
In this paper, we explore the usage of hierarchical priors to improve learning in contexts where the number of available examples is extremely low.Specifically, we consider a Prototype Learning setting where deep neural networks are used to embed data in hyperspherical geometries.In this scenario, we propose an innovative way to learn the prototypes by combining class separation and hierarchical information.In addition, we introduce a contrastive loss function capable of balancing the exploitation of prototypes through a prototype pruning mechanism.We compare the proposed method with state-of-the-art approaches on two public datasets.This work has been partially supported by the Spoke 1 "FutureHPC & BigData" of ICSC -Centro Nazionale di Ricerca in High-Performance-Computing, Big Data and Quantum Computing, funded by European Union -NextGenerationEU.