6 citations · 9 across the 9 of their papers we have counts for
10 papers
Mitigating Domain Shift in Conditioned Floor Plan Generation: Synthetic Pre-training for Data-Efficient Adaptation
Matthieu Ospici, Arnaud Gueze, Luc Bourrat +1
Robustness to domain shift is a key requirement for floor plan generative models to be applicable beyond the single dataset they were trained on, as floor plans vary widely across…
Retrieval-Enhanced Real Estate Appraisal
Simon Popelier, Matthieu X. B. Sarazin, Maximilien Bohm +4
The Sales Comparison Approach (SCA) is one of the most popular when it comes to real estate appraisal. Used as a reference in real estate expertise and as one of the major types of…
Online Unsupervised Domain Adaptation for Person Re-identification
Hamza Rami, Matthieu Ospici, Stéphane Lathuilière
Unsupervised domain adaptation for person re-identification (Person Re-ID) is the task of transferring the learned knowledge on the labeled source domain to the unlabeled target do…
Prediction of fish location by combining fisheries data and sea bottom temperature forecasting
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 so…
Using Synthetic Corruptions to Measure Robustness to Natural Distribution Shifts
Alfred Laugros, Alice Caplier, Matthieu Ospici
Synthetic corruptions gathered into a benchmark are frequently used to measure neural network robustness to distribution shifts. However, robustness to synthetic corruption benchma…
Using the Overlapping Score to Improve Corruption Benchmarks
Alfred Laugros, Alice Caplier, Matthieu Ospici
Neural Networks are sensitive to various corruptions that usually occur in real-world applications such as blurs, noises, low-lighting conditions, etc. To estimate the robustness o…
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training
Alfred Laugros, Alice Caplier, Matthieu Ospici
Despite their performance, Artificial Neural Networks are not reliable enough for most of industrial applications. They are sensitive to noises, rotations, blurs and adversarial ex…
Are Adversarial Robustness and Common Perturbation Robustness Independent Attributes ?
Alfred Laugros, Alice Caplier, Matthieu Ospici
Neural Networks have been shown to be sensitive to common perturbations such as blur, Gaussian noise, rotations, etc. They are also vulnerable to some artificial malicious corrupti…
Person re-identification across different datasets with multi-task learning
Matthieu Ospici, Antoine Cecchi
This paper presents an approach to tackle the re-identification problem. This is a challenging problem due to the large variation of pose, illumination or camera view. More and mor…
Density Functional Theory calculation on many-cores hybrid CPU-GPU architectures
Luigi Genovese, Matthieu Ospici, Thierry Deutsch +3
The implementation of a full electronic structure calculation code on a hybrid parallel architecture with Graphic Processing Units (GPU) is presented. The code which is on the basi…