  Image recognition with prototypes is considered an interpretable alternative
for black box deep learning models. Classification depends on the extent to
which a test image "looks like" a prototype. However, perceptual similarity for
humans can be different from the similarity learned by the classification
model. Hence, only visualising prototypes can be insufficient for a user to
understand what a prototype exactly represents, and why the model considers a
prototype and an image to be similar. We address this ambiguity and argue that
prototypes should be explained. We improve interpretability by automatically
enhancing visual prototypes with textual quantitative information about visual
characteristics deemed important by the classification model. Specifically, our
method clarifies the meaning of a prototype by quantifying the influence of
colour hue, shape, texture, contrast and saturation and can generate both
global and local explanations. Because of the generality of our approach, it
can improve the interpretability of any similarity-based method for
prototypical image recognition. In our experiments, we apply our method to the
existing Prototypical Part Network (ProtoPNet). Our analysis confirms that the
global explanations are generalisable, and often correspond to the visually
perceptible properties of a prototype. Our explanations are especially relevant
for prototypes which might have been interpreted incorrectly otherwise. By
explaining such 'misleading' prototypes, we improve the interpretability and
simulatability of a prototype-based classification model. We also use our
method to check whether visually similar prototypes have similar explanations,
and are able to discover redundancy. Code is available at
https://github.com/M-Nauta/Explaining_Prototypes .
