Interpretability and Explainability of Deep Models
The concept of explainability has several facets and the need for explainability is strong in safety-critical applications such as autonomous driving where deep learning models are now widely used. As the underlying mechanisms of these models remain opaque, explainability and trustworthiness have become major concerns. Among other things, we investigate methods providing explanations to a black-box visual-based systems in a post-hoc fashion, as well as approaches that aim at building more interpretable self-driving systems by design.
Mehdi Zemni, Mickaël Chen, Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, Matthieu Cord
Computer Vision and Pattern Recognition (CVPR), 2023
Paul Jacob, Éloi Zablocki, Hédi Ben-Younes, Mickaël Chen, Patrick Pérez, Matthieu Cord
European Conference on Computer Vision, 2022
Éloi Zablocki*, Hédi Ben-Younes*, Patrick Pérez, Matthieu Cord
International Journal of Computer Vision, 2022