3 citations · 5 across the 8 of their papers we have counts for
9 papers
Beyond : Generalizing Abductive Latent Explanations to Diverse Prototype-Based Architectures
Jules Soria, Alban Grastien, Romain Xu-Darme +3
Prototype-based neural networks are hailed as interpretable-by-design architectures. Recently, Abductive Latent Explanations (ALE) were introduced to provide formal, mathematically…
Formal Abductive Latent Explanations for Prototype-Based Networks
Jules Soria, Zakaria Chihani, Julien Girard-Satabin +3
Case-based reasoning networks are machine-learning models that make predictions based on similarity between the input and prototypical parts of training samples, called prototypes.…
CaBRNet, an open-source library for developing and evaluating Case-Based Reasoning Models
Romain Xu-Darme, Aymeric Varasse, Alban Grastien +2
In the field of explainable AI, a vibrant effort is dedicated to the design of self-explainable models, as a more principled alternative to post-hoc methods that attempt to explain…
Sanity checks for patch visualisation in prototype-based image classification
Romain Xu-Darme, Georges Quénot, Zakaria Chihani +1
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 s…
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…
Contextualised Out-of-Distribution Detection using Pattern Identication
Romain Xu-Darme, Julien Girard-Satabin, Darryl Hond +2
In this work, we propose CODE, an extension of existing work from the field of explainable AI that identifies class-specific recurring patterns to build a robust Out-of-Distributio…
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…
Interpretable Out-Of-Distribution Detection Using Pattern Identification
Romain Xu-Darme, Julien Girard-Satabin, Darryl Hond +2
Out-of-distribution (OoD) detection for data-based programs is a goal of paramount importance. Common approaches in the literature tend to train detectors requiring inside-of-distr…
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…