5 papers · 1 filter
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…
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…
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…