5 citations · 13 across the 5 of their papers we have counts for
6 papers
Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization
Thomas Fel, Thibaut Boissin, Victor Boutin +9
Feature visualization has gained substantial popularity, particularly after the influential work by Olah et al. in 2017, which established it as a crucial tool for explainability.…
Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?
Victor Boutin, Thomas Fel, Lakshya Singhal +4
An important milestone for AI is the development of algorithms that can produce drawings that are indistinguishable from those of humans. Here, we adapt the 'diversity vs. recogniz…
CRAFT: Concept Recursive Activation FacTorization for Explainability
Thomas Fel, Agustin Picard, Louis Bethune +5
Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity as a type of explaina…
A Benchmark for Compositional Visual Reasoning
Aimen Zerroug, Mohit Vaishnav, Julien Colin +2
A fundamental component of human vision is our ability to parse complex visual scenes and judge the relations between their constituent objects. AI benchmarks for visual reasoning…
Xplique: A Deep Learning Explainability Toolbox
Thomas Fel, Lucas Hervier, David Vigouroux +12
Today's most advanced machine-learning models are hardly scrutable. The key challenge for explainability methods is to help assisting researchers in opening up these black boxes, b…
What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods
Julien Colin, Thomas Fel, Remi Cadene +1
A multitude of explainability methods and associated fidelity performance metrics have been proposed to help better understand how modern AI systems make decisions. However, much o…