Neural Prototype Trees for Interpretable Fine-grained Image Recognition

Understand

Prototype-based methods use interpretable representations to address the black-box nature of deep learning models, in contrast to post-hoc explanation methods that only approximate such models.

• We propose the Neural Prototype Tree (ProtoTree), an intrinsically interpretable deep learning method for fine-grained image recognition.
• ProtoTree combines prototype learning with decision trees, and thus results in a globally interpretable model by design.
• Additionally, ProtoTree can locally explain a single prediction by outlining a decision path through the tree.