Prototype Network

Prototype networks are a class of machine learning models designed to improve the interpretability of deep learning models, primarily by identifying and utilizing representative "prototypes" from the training data to explain predictions. Current research focuses on enhancing the accuracy and faithfulness of these explanations, addressing challenges like the semantic gap between feature and input spaces, and developing efficient architectures for various applications, including image recognition and natural language processing. This work is significant because it aims to bridge the gap between the high accuracy of "black box" models and the need for transparent and understandable decision-making processes, particularly in high-stakes domains like medicine.