What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods. - Julien Colin; Thomas Fel; Rémi Cadène; Thomas Serre (NeurIPS, 2022)

A Benchmark for Compositional Visual Reasoning. - Aimen Zerroug; Mohit Vaishnav; Julien Colin; Sebastian Musslick; Thomas Serre (NeurIPS, 2022)

Xplique: A Deep Learning Explainability Toolbox. - Thomas Fel; Lucas Hervier; David Vigouroux; Antonin Poché; Justin Plakoo; Rémi Cadène; Mathieu Chalvidal; Julien Colin; Thibaut Boissin; Louis Béthune; Agustin Martin Picard; Claire Nicodeme; et al. (CoRR, 2022)

CRAFT: Concept Recursive Activation FacTorization for Explainability. - Thomas Fel; Agustin Martin Picard; Louis Béthune; Thibaut Boissin; David Vigouroux; Julien Colin; Rémi Cadène; Thomas Serre (CVPR, 2023)

Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines? - Victor Boutin; Thomas Fel; Lakshya Singhal; Rishav Mukherji; Akash Nagaraj; Julien Colin; Thomas Serre (ICML, 2023)

Unlocking Feature Visualization for Deep Network with MAgnitude Constrained Optimization. - Thomas Fel; Thibaut Boissin; Victor Boutin; Agustin Picard; Paul Novello; Julien Colin; Drew Linsley; Tom Rousseau; Rémi Cadène; Lore Goetschalckx; Laurent Gardes; Thomas Serre (NeurIPS, 2023)

Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization. - Thomas Fel; Thibaut Boissin; Victor Boutin; Agustin Martin Picard; Paul Novello; Julien Colin; Drew Linsley; Tom Rousseau; Rémi Cadène; Laurent Gardes; Thomas Serre (CoRR, 2023)

Local vs distributed representations: What is the right basis for interpretability? - Julien Colin; Lore Goetschalckx; Thomas Fel; Victor Boutin; Jay Gopal; Thomas Serre; Nuria Oliver (CoRR, 2024)