  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. Each node in our binary tree contains a
trainable prototypical part. The presence or absence of this learned prototype
in an image determines the routing through a node. Decision making is therefore
similar to human reasoning: Does the bird have a red throat? And an elongated
beak? Then it's a hummingbird! We tune the accuracy-interpretability trade-off
using ensemble methods, pruning and binarizing. We apply pruning without
sacrificing accuracy, resulting in a small tree with only 8 learned prototypes
along a path to classify a bird from 200 species. An ensemble of 5 ProtoTrees
achieves competitive accuracy on the CUB-200- 2011 and Stanford Cars data sets.
Code is available at https://github.com/M-Nauta/ProtoTree
