17 citations · 36 across the 9 of their papers we have counts for
11 papers
Do Knowledge and Reasoning Oriented Benchmarks Induce Stable Representation Signatures in LLMs?
Parsa Rahimi, Azim Dehghani Amirabad
Internal signatures could support monitoring, adaptive computation, and mechanistic analysis, but benchmark prompts can be separable without any shared computational distinction. W…
ScoreShield: Differentially Private Release of Similarity Scores
Behrooz Razeghi, Parsa Rahimi
A growing number of applications, such as biometrics and retrieval-augmented generation (RAG), rely on cosine similarity scores computed between vector embeddings of text, images,…
ScoreMix: Synthetic Data Generation by Score Composition in Diffusion Models Improves Recognition
Parsa Rahimi, Sebastien Marcel
Synthetic data generation is increasingly used in machine learning for training and data augmentation. Yet, current strategies often rely on external foundation models or datasets,…
AugGen: Synthetic Augmentation using Diffusion Models Can Improve Recognition
Parsa Rahimi, Damien Teney, Sebastien Marcel
The increasing reliance on large-scale datasets in machine learning poses significant privacy and ethical challenges, particularly in sensitive domains such as face recognition. Sy…
Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to Improve Face Recognition with Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +56
Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including…
Synthetic to Authentic: Transferring Realism to 3D Face Renderings for Boosting Face Recognition
Parsa Rahimi, Behrooz Razeghi, Sebastien Marcel
In this paper, we investigate the potential of image-to-image translation (I2I) techniques for transferring realism to 3D-rendered facial images in the context of Face Recognition…
Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data
Ivan DeAndres-Tame, Ruben Tolosana, Pietro Melzi +55
Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class v…
Deep Privacy Funnel Model: From a Discriminative to a Generative Approach with an Application to Face Recognition
Behrooz Razeghi, Parsa Rahimi, Sébastien Marcel
In this study, we apply the information-theoretic Privacy Funnel (PF) model to face recognition and develop a method for privacy-preserving representation learning within an end-to…
Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition
Behrooz Razeghi, Parsa Rahimi, Sébastien Marcel
In this study, we harness the information-theoretic Privacy Funnel (PF) model to develop a method for privacy-preserving representation learning using an end-to-end training framew…
FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
Pietro Melzi, Ruben Tolosana, Ruben Vera-Rodriguez +44
Despite the widespread adoption of face recognition technology around the world, and its remarkable performance on current benchmarks, there are still several challenges that must…
Toward responsible face datasets: modeling the distribution of a disentangled latent space for sampling face images from demographic groups
Parsa Rahimi, Christophe Ecabert, Sebastien Marcel
Recently, it has been exposed that some modern facial recognition systems could discriminate specific demographic groups and may lead to unfair attention with respect to various fa…