Sanity checks for patch visualisation in prototype-based image classification

Romain Xu-Darme (1, 2, 3, 4, 5, 6) , Georges Quénot (2) , Zakaria Chihani (1, 3, 4, 5, 6) , Marie-Christine Rousset (2)

1 LSL - Laboratoire Sûreté des Logiciels
2 LIG - Laboratoire d'Informatique de Grenoble
3 DILS (CEA, LIST) - Département Ingénierie Logiciels et Systèmes
4 LIST (CEA) - Laboratoire d'Intégration des Systèmes et des Technologies
5 DRT (CEA) - Direction de Recherche Technologique (CEA)
6 Université Paris-Saclay

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.

Domaines: Intelligence artificielle [cs.AI]; Réseau de neurones [cs.NE]

Romain Xu-Darme, Georges Quénot, Zakaria Chihani, Marie-Christine Rousset. Sanity checks for patch visualisation in prototype-based image classification. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Jun 2023, Vancouvers, Canada. ⟨cea-04253851v3⟩