Prototype Classifier

Prototype classifiers are machine learning models that categorize data points based on their proximity to learned class prototypes, aiming for efficient and interpretable classification. Current research focuses on improving prototype learning in challenging scenarios like class-incremental learning (where new classes are added continuously) and long-tailed recognition (where class frequencies are highly imbalanced), often employing techniques like contrastive learning and adaptive prototype adjustments. These advancements enhance model robustness, reduce computational demands, and improve performance in data-scarce or noisy environments, impacting fields such as image recognition and clustering.