5 papers
MONET: A Massive, Open, Non-redundant and Enriched Text-to-image dataset
Benjamin Aubin, Gonzalo Iñaki Quintana, Onur Tasar +4
Training large text-to-image models requires high-quality, curated datasets with diverse content and detailed captions. Yet the cost and complexity of collecting, filtering, dedupl…
Evaluating Federated Learning approaches for mammography under breast density heterogeneity
Gonzalo Iñaki Quintana, Franco Martin Di Maria, Laurence Vancamberg
Breast density is a key factor that influences mammography interpretation and is a major source of heterogeneity in multicenter datasets. Such heterogeneity poses challenges for co…
Fast, faithful and photorealistic diffusion-based image super-resolution with enhanced Flow Map models
Maxence Noble, Gonzalo Iñaki Quintana, Benjamin Aubin +1
Diffusion-based image super-resolution (SR) has recently attracted significant attention by leveraging the expressive power of large pre-trained text-to-image diffusion models (DMs…
Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application
Gonzalo Iñaki Quintana, Laurence Vancamberg, Vincent Jugnon +2
This work studies the relationship between Contrastive Learning and Domain Adaptation from a theoretical perspective. The two standard contrastive losses, NT-Xent loss (Self-superv…
BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in Federated Learning
Gonzalo Iñaki Quintana, Laurence Vancamberg, Vincent Jugnon +2
Federated Learning (FL) is gaining traction as a learning paradigm for training Machine Learning (ML) models in a decentralized way. Batch Normalization (BN) is ubiquitous in Deep…