no code implementations • 19 Mar 2024 • Guoxuan Xia, Olivier Laurent, Gianni Franchi, Christos-Savvas Bouganis
We first demonstrate empirically across a range of tasks and architectures that LS leads to a consistent degradation in SC.
no code implementations • 23 Dec 2023 • Gianni Franchi, Olivier Laurent, Maxence Leguéry, Andrei Bursuc, Andrea Pilzer, Angela Yao
Deep Neural Networks (DNNs) are powerful tools for various computer vision tasks, yet they often struggle with reliable uncertainty quantification - a critical requirement for real-world applications.
1 code implementation • 12 Oct 2023 • Olivier Laurent, Emanuel Aldea, Gianni Franchi
The distribution of the weights of modern deep neural networks (DNNs) - crucial for uncertainty quantification and robustness - is an eminently complex object due to its extremely high dimensionality.
no code implementations • 1 Aug 2023 • Marwane Hariat, Olivier Laurent, Rémi Kazmierczak, Shihao Zhang, Andrei Bursuc, Angela Yao, Gianni Franchi
We propose a novel approach to improve the robustness of semantic segmentation techniques by leveraging the synergy between label-to-image generators and image-to-label segmentation models.
1 code implementation • 17 Oct 2022 • Olivier Laurent, Adrien Lafage, Enzo Tartaglione, Geoffrey Daniel, Jean-Marc Martinez, Andrei Bursuc, Gianni Franchi
Deep Ensembles (DE) are a prominent approach for achieving excellent performance on key metrics such as accuracy, calibration, uncertainty estimation, and out-of-distribution detection.