1 code implementation • 14 Nov 2022 • Charles Corbière
The last decade's research in artificial intelligence had a significant impact on the advance of autonomous driving.
no code implementations • 26 Aug 2022 • Simon Roburin, Charles Corbière, Gilles Puy, Nicolas Thome, Matthieu Aubry, Renaud Marlet, Patrick Pérez
Predictive performance of machine learning models trained with empirical risk minimization (ERM) can degrade considerably under distribution shifts.
no code implementations • 29 Sep 2021 • Charles Corbière, Marc Lafon, Nicolas Thome, Matthieu Cord, Patrick Perez
A crucial property of KLoS is to be a class-wise divergence measure built from in-distribution samples and to not require OOD training data, in contrast to current second-order uncertainty measures.
no code implementations • 11 Dec 2020 • Charles Corbière, Nicolas Thome, Antoine Saporta, Tuan-Hung Vu, Matthieu Cord, Patrick Pérez
In this paper, we introduce a novel target criterion for model confidence, namely the true class probability (TCP).
1 code implementation • NeurIPS 2019 • Charles Corbière, Nicolas Thome, Avner Bar-Hen, Matthieu Cord, Patrick Pérez
In this paper, we propose a new target criterion for model confidence, corresponding to the True Class Probability (TCP).
1 code implementation • NeurIPS 2019 • Charles Corbière, Nicolas Thome, Avner Bar-Hen, Matthieu Cord, Patrick Pérez
In this paper, we propose a new target criterion for model confidence, corresponding to the True Class Probability (TCP).
no code implementations • 27 Sep 2017 • Charles Corbière, Hedi Ben-Younes, Alexandre Ramé, Charles Ollion
In this paper, we present a method to learn a visual representation adapted for e-commerce products.