Adversarial Learning for Feature Shift Detection and Correction

1 code implementation • NeurIPS 2023 • Miriam Barrabes, Daniel Mas Montserrat, Margarita Geleta, Xavier Giro-i-Nieto, Alexander G. Ioannidis

Data shift is a phenomenon present in many real-world applications, and while there are multiple methods attempting to detect shifts, the task of localizing and correcting the features originating such shifts has not been studied in depth.

Maestro: A Gamified Platform for Teaching AI Robustness

no code implementations • 14 Jun 2023 • Margarita Geleta, Jiacen Xu, Manikanta Loya, Junlin Wang, Sameer Singh, Zhou Li, Sergio Gago-Masague

We assessed Maestro's influence on students' engagement, motivation, and learning success in robust AI.

Active Learning

Towards Robust Image-in-Audio Deep Steganography

1 code implementation • 9 Mar 2023 • Jaume Ros, Margarita Geleta, Jordi Pons, Xavier Giro-i-Nieto

The field of steganography has experienced a surge of interest due to the recent advancements in AI-powered techniques, particularly in the context of multimodal setups that enable the concealment of signals within signals of a different nature.

Image Reconstruction

MT-Adapted Datasheets for Datasets: Template and Repository

no code implementations • 27 May 2020 • Marta R. Costa-jussà, Roger Creus, Oriol Domingo, Albert Domínguez, Miquel Escobar, Cayetana López, Marina Garcia, Margarita Geleta

In this report we are taking the standardized model proposed by Gebru et al. (2018) for documenting the popular machine translation datasets of the EuroParl (Koehn, 2005) and News-Commentary (Barrault et al., 2019).

Machine Translation Translation