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. We investigate methods providing post-hoc explanations to black-box systems, and approaches to directly design more interpretable models.

Selected publications

  1. Mehdi Zemni, Mickaël Chen, Éloi Zablocki, Hédi Ben-Younes, Patrick Pérez, Matthieu Cord
    Computer Vision and Pattern Recognition, 2023
  2. Paul Jacob, Éloi Zablocki, Hédi Ben-Younes, Mickaël Chen, Patrick Pérez, Matthieu Cord
    European Conference on Computer Vision, 2022

  3. Éloi Zablocki*, Hédi Ben-Younes*, Patrick Pérez, Matthieu Cord
    International Journal of Computer Vision, 2022

  4. Hédi Ben-Younes*, Éloi Zablocki*, Patrick Pérez, Matthieu Cord
    Pattern Recognition (PR), 2022