Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions

Understand

Deep neural networks are widely used for classification.

• These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of their non-linear nature.
• As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to optimize the accuracy of predictions.
• In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction.