Hédi Ben-Younes — accepted AI-conference papers

2023

OCTET: Object-Aware Counterfactual Explanations

Mehdi Zemni, Mickaël Chen, Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, Matthieu Cord

CVPR 2023poster

Nowadays, deep vision models are being widely deployed in safety-critical applications, e.g., autonomous driving, and explainability of such models is becoming a pressing concern. Among explanation methods, counterfactual explanations aim to find minimal and interpretable changes to the input image…

Cited by 40 Source ↗PDF ↗code ↗Scholar ↗

2022

STEEX: Steering Counterfactual Explanations with Semantics

Paul Jacob, Éloi Zablocki, Hédi Ben-Younes, Mickaël Chen, Patrick Pérez, Matthieu Cord

ECCV 2022poster

"As deep learning models are increasingly used in safety-critical applications, explainability and trustworthiness become major concerns. For simple images, such as low-resolution face portraits, synthesizing visual counterfactual explanations has recently been proposed as a way to uncover the decis…

Cited by 57 Source ↗PDF ↗code ↗Scholar ↗