Neural Prototype Trees for Interpretable Fine-grained Image Recognition
Interpretable machine learning addresses the black-box nature of deep neural networks. Visual prototypes have been suggested for intrinsically interpretable image recognition, instead of generating post-hoc explanations that approximate a trained model...
However, a large number of prototypes can be overwhelming. To reduce explanation size and improve interpretability, we propose the Neural Prototype Tree (ProtoTree), a deep learning method that includes prototypes in an interpretable decision tree to faithfully visualize the entire model. In addition to global interpretability, a path in the tree explains a single prediction. Each node in our binary tree contains a trainable prototypical