Sanity checks for patch visualisation in prototype-based image
  classification
Romain Xu-Darme Georges Quénot Zakaria Chihani Marie-Christine Rousset
In this work, we perform an analysis of the visualisation methods implemented
in ProtoPNet and ProtoTree, two self-explaining visual classifiers based on
prototypes. We show that such methods do not correctly identify the regions of
interest inside of the images, and therefore do not reflect the model
behaviour, which can create a false sense of bias in the model. We also
demonstrate quantitatively that this issue can be mitigated by using other
saliency methods that provide more faithful image patches.