9 citations · 12 across the 4 of their papers we have counts for
5 papers
Conformal Semantic Image Segmentation: Post-hoc Quantification of Predictive Uncertainty
Luca Mossina, Joseba Dalmau, Léo andéol
We propose a post-hoc, computationally lightweight method to quantify predictive uncertainty in semantic image segmentation. Our approach uses conformal prediction to generate stat…
Saliency strikes back: How filtering out high frequencies improves white-box explanations
Sabine Muzellec, Thomas Fel, Victor Boutin +3
Attribution methods correspond to a class of explainability methods (XAI) that aim to assess how individual inputs contribute to a model's decision-making process. We have identifi…
A Holistic Approach to Unifying Automatic Concept Extraction and Concept Importance Estimation
Thomas Fel, Victor Boutin, Mazda Moayeri +5
In recent years, concept-based approaches have emerged as some of the most promising explainability methods to help us interpret the decisions of Artificial Neural Networks (ANNs).…
Confident Object Detection via Conformal Prediction and Conformal Risk Control: an Application to Railway Signaling
Léo Andéol, Thomas Fel, Florence De Grancey +1
Deploying deep learning models in real-world certified systems requires the ability to provide confidence estimates that accurately reflect their uncertainty. In this paper, we dem…
Conformal Prediction for Trustworthy Detection of Railway Signals
Léo Andéol, Thomas Fel, Florence De Grancey +1
We present an application of conformal prediction, a form of uncertainty quantification with guarantees, to the detection of railway signals. State-of-the-art architectures are tes…