3 citations · 4 across the 3 of their papers we have counts for
3 papers
On the stability, correctness and plausibility of visual explanation methods based on feature importance
Romain Xu-Darme, Jenny Benois-Pineau, Romain Giot +4
In the field of Explainable AI, multiples evaluation metrics have been proposed in order to assess the quality of explanation methods w.r.t. a set of desired properties. In this wo…
Sanity checks and improvements for patch visualisation in prototype-based image classification
Romain Xu-Darme, Georges Quénot, Zakaria Chihani +1
In this work, we perform an in-depth analysis of the visualisation methods implemented in two popular self-explaining models for visual classification based on prototypes - ProtoPN…
PARTICUL: Part Identification with Confidence measure using Unsupervised Learning
Romain Xu-Darme, Georges Quénot, Zakaria Chihani +1
In this paper, we present PARTICUL, a novel algorithm for unsupervised learning of part detectors from datasets used in fine-grained recognition. It exploits the macro-similarities…