5 citations · 13 across the 12 of their papers we have counts for
12 papers
On Using Certified Training towards Empirical Robustness
Alessandro De Palma, Serge Durand, Zakaria Chihani +2
Adversarial training is arguably the most popular way to provide empirical robustness against specific adversarial examples. While variants based on multi-step attacks incur signif…
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…
CAISAR: A platform for Characterizing Artificial Intelligence Safety and Robustness
Julien Girard-Satabin, Michele Alberti, François Bobot +2
We present CAISAR, an open-source platform under active development for the characterization of AI systems' robustness and safety. CAISAR provides a unified entry point for definin…
DISCO Verification: Division of Input Space into COnvex polytopes for neural network verification
Julien Girard-Satabin, Aymeric Varasse, Marc Schoenauer +2
The impressive results of modern neural networks partly come from their non linear behaviour. Unfortunately, this property makes it very difficult to apply formal verification tool…
CAMUS: A Framework to Build Formal Specifications for Deep Perception Systems Using Simulators
Julien Girard-Satabin, Guillaume Charpiat, Zakaria Chihani +1
The topic of provable deep neural network robustness has raised considerable interest in recent years. Most research has focused on adversarial robustness, which studies the robust…
CDCL-inspired Word-level Learning for Bit-vector Constraint Solving
Zakaria Chihani, François Bobot, Sébastien Bardin
The theory of quantifier-free bit-vectors (QF_BV) is of paramount importance in software verification. The standard approach for satisfiability checking reduces the bit-vector prob…