9 papers
Robust Detection of Synthetic Tabular Data under Schema Variability
G. Charbel N. Kindji, Elisa Fromont, Lina Maria Rojas-Barahona +1
The rise of powerful generative models has sparked concerns over data authenticity. While detection methods have been extensively developed for images and text, the case of tabular…
FSMODNet: A Closer Look at Few-Shot Detection in Multispectral Data
Manuel Nkegoum, Minh-Tan Pham, Ãlisa Fromont +2
Few-shot multispectral object detection (FSMOD) addresses the challenge of detecting objects across visible and thermal modalities with minimal annotated data. In this paper, we ex…
Tabular Data Generation Models: An In-Depth Survey and Performance Benchmarks with Extensive Tuning
G. Charbel N. Kindji, Lina Maria Rojas-Barahona, Elisa Fromont +1
The ability to train generative models that produce realistic, safe and useful tabular data is essential for data privacy, imputation, oversampling, explainability or simulation. H…
Datum-wise Transformer for Synthetic Tabular Data Detection in the Wild
G. Charbel N. Kindji, Elisa Fromont, Lina Maria Rojas-Barahona +1
The growing power of generative models raises major concerns about the authenticity of published content. To address this problem, several synthetic content detection methods have…
Synthetic Tabular Data Detection In the Wild
G. Charbel N. Kindji, Elisa Fromont, Lina Maria Rojas-Barahona +1
Detecting synthetic tabular data is essential to prevent the distribution of false or manipulated datasets that could compromise data-driven decision-making. This study explores wh…
Mitigating analytical variability in fMRI results with style transfer
Elodie Germani, Camille Maumet, Elisa Fromont
We propose a novel approach to improve the reproducibility of neuroimaging results by converting statistic maps across different functional MRI pipelines. We make the assumption th…