9 papers
Benchmarking Hyperspectral Foundation Models for Hyperspectral Unmixing
Edgard Dabier, Christophe Kervazo, Pietro Gori +1
Several foundation models dedicated to hyperspectral images have recently been made available. These models are trained on large unlabeled datasets and exhibit strong performance o…
Geometric-to-Semantic Spherical Transfer Learning for Cortical Sulci Labeling
Saeb Tounsi, Joël Chavas, Pietro Gori +3
Deep learning on cortical surfaces faces a dilemma: capturing the complex topology of over 60 nomenclature-dependent sulci per hemisphere requires high-capacity models, yet the ext…
Representation learning of human cortical folding to reveal long lasting neurodevelopmental signatures
Julien Laval, Robin Guiavarch, Antoine Dufournet +24
The human brain folds in utero, primarily during late gestation. Shortly after birth, cortical folding patterns are established and remain stable thereafter, making them promising…
Counterfactual Contrastive Analysis
Yunlong He, Pietro Gori
Visual Counterfactual Explanations (VCEs) aim to explain image classifiers by generating minimally edited and realistic versions of an input image that change the classifier's pred…
Diff-CA: Separating Common and Salient Factors with Diffusion Models
Michaël Soumm, Alexandre Fournier Montgieux, Yunlong He +2
Contrastive Analysis aims to separate factors that are common between two data distributions from those that are salient to only one of them. Existing contrastive methods are based…
Automatic Discovery of Disease Subgroups by Contrasting with Healthy Controls
Robin Louiset, Edouard Duchesnay, Benoit Dufumier +2
In biomedical Subgroup Discovery, practitioners are interested in discovering interpretable and homogeneous subgroups within a group of patients. In this paper, assuming that healt…
Robust brain age estimation from structural MRI with contrastive learning
Carlo Alberto Barbano, Benoit Dufumier, Edouard Duchesnay +2
Estimating brain age from structural MRI has emerged as a powerful tool for characterizing normative and pathological aging. In this work, we explore contrastive learning as a scal…
Separating common from salient patterns with Contrastive Representation Learning
Robin Louiset, Edouard Duchesnay, Antoine Grigis +1
Contrastive Analysis is a sub-field of Representation Learning that aims at separating common factors of variation between two datasets, a background (i.e., healthy subjects) and a…
Double InfoGAN for Contrastive Analysis
Florence Carton, Robin Louiset, Pietro Gori
Contrastive Analysis (CA) deals with the discovery of what is common and what is distinctive of a target domain compared to a background one. This is of great interest in many appl…