  In this paper, we introduce ProtoPShare, a self-explained method that
incorporates the paradigm of prototypical parts to explain its predictions. The
main novelty of the ProtoPShare is its ability to efficiently share
prototypical parts between the classes thanks to our data-dependent
merge-pruning. Moreover, the prototypes are more consistent and the model is
more robust to image perturbations than the state of the art method ProtoPNet.
We verify our findings on two datasets, the CUB-200-2011 and the Stanford Cars.
