Prototype Selection

Prototype selection focuses on identifying a representative subset of data points (prototypes) that effectively capture the underlying data distribution, aiming to improve efficiency and interpretability in machine learning models. Current research emphasizes developing algorithms that efficiently select prototypes, often incorporating techniques like multi-armed bandits or generative modeling to improve scalability and accuracy, and exploring the use of prototypes in various applications such as class incremental learning, few-shot learning, and domain adaptation. This research area is significant because efficient and effective prototype selection can reduce computational costs, enhance model interpretability, and improve the generalization performance of machine learning models across diverse tasks and domains.