Robust Few-Shot Ensemble Learning with Focal Diversity-Based Pruning

arXiv:2404.04434v1 Announce Type: cross
Abstract: This paper presents FusionShot, a focal diversity optimized few-shot ensemble learning approach for boosting the robustness and generalization performance of pre-trained few-shot models. The paper makes three original contributions. First, we explore the unique characteristics of few-shot learning to ensemble multiple few-shot (FS) models by creating three alternative fusion channels. Second, we introduce the concept of focal error diversity to learn the most efficient ensemble teaming strategy, rather than assuming that an ensemble of a …

arxiv cs.cv cs.lg diversity ensemble few-shot pruning robust type