Prototype Based

Prototype-based methods are gaining traction in machine learning, aiming to improve model interpretability and efficiency by representing data classes with a small set of representative prototypes. Current research focuses on developing novel prototype-based architectures for various tasks, including classification, regression, and continual learning, often incorporating techniques like neural additive models, information bottleneck principles, and vector quantization to enhance performance and explainability. This approach offers significant advantages in applications requiring transparent decision-making, such as medical image analysis and robotics, while also addressing challenges like catastrophic forgetting in continual learning scenarios.