Visual Prototype

Visual prototypes are representative data points used in machine learning to improve model performance, interpretability, and efficiency, particularly in scenarios with limited labeled data or imbalanced classes. Current research focuses on developing and applying prototype-based methods across diverse domains, including sound event detection, biological image analysis, and medical image classification, often employing architectures like prototypical networks and transformer models. This approach offers significant advantages in explainable AI, few-shot learning, and handling data scarcity, leading to improved model accuracy and trustworthiness in various applications.