Causes of Rejects in Prototype-based Classification Aleatoric vs. Epistemic Uncertainty
Johannes Brinkrolf, Valerie Vaquet, Fabian Hinder, Barbara Hammer · 2024
Prototype-based methods constitute a robust and transparent family of machine-learning models.To increase robustness in real-world applications, they are frequently coupled with reject options.While the state-of-the-art method, relative similarity, couples the rejection of samples with high aleatoric and epistemic uncertainty, the technique lacks transparency, i.e., an explanation of why a sample has been rejected.In this work, we analyze the relative similarity analytically and derive an explanation scheme for reject options in prototype-based classification.