Multi Prototype

Multi-prototype learning is a machine learning approach that uses multiple representative examples (prototypes) per class to improve model accuracy and interpretability, addressing limitations of single-prototype methods which often fail to capture the inherent complexity of data distributions. Current research focuses on developing algorithms that effectively learn and utilize these multiple prototypes within various model architectures, including those based on deep embedding clustering, contrastive learning, and Gaussian Mixture Models, often applied to anomaly detection, federated learning, and domain adaptation tasks. This approach enhances model performance, particularly in scenarios with high data variability or limited labeled data, and provides more insightful explanations of model predictions, leading to improved trustworthiness and applicability in diverse fields.