no code implementations • 9 May 2022 • Hamza Rami, Matthieu Ospici, Stéphane Lathuilière
Therefore, we present a new yet practical online setting for Unsupervised Domain Adaptation for person Re-ID with two main constraints: Online Adaptation and Privacy Protection.
Online unsupervised domain adaptation Person Re-Identification
no code implementations • 4 May 2022 • Matthieu Ospici, Klaas Sys, Sophie Guegan-Marat
This paper combines fisheries dependent data and environmental data to be used in a machine learning pipeline to predict the spatio-temporal abundance of two species (plaice and sole) commonly caught by the Belgian fishery in the North Sea.
1 code implementation • 26 Jul 2021 • Alfred Laugros, Alice Caplier, Matthieu Ospici
Using the overlapping criterion, we split synthetic corruptions into categories that help to better understand neural network robustness.
no code implementations • 26 May 2021 • Alfred Laugros, Alice Caplier, Matthieu Ospici
To estimate the robustness of neural networks to these common corruptions, we generally use a group of modeled corruptions gathered into a benchmark.
no code implementations • 1 Jan 2021 • Alfred Laugros, Alice Caplier, Matthieu Ospici
In this paper, we propose to build corruption benchmarks with only non-overlapping corruptions, to improve their coverage and their balance.
no code implementations • 19 Aug 2020 • Alfred Laugros, Alice Caplier, Matthieu Ospici
Despite their performance, Artificial Neural Networks are not reliable enough for most of industrial applications.
no code implementations • 4 Sep 2019 • Alfred Laugros, Alice Caplier, Matthieu Ospici
We intend to study the links between the robustnesses of neural networks to both perturbations.
no code implementations • 25 Jul 2018 • Matthieu Ospici, Antoine Cecchi
We show that with our method, we are able to build a system that performs well on different datasets and simultaneously extracts attributes.