4 citations · 9 across the 6 of their papers we have counts for
6 papers
Normalizing flow sampling with Langevin dynamics in the latent space
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
Normalizing flows (NF) use a continuous generator to map a simple latent (e.g. Gaussian) distribution, towards an empirical target distribution associated with a training data set.…
Plug-and-Play split Gibbs sampler: embedding deep generative priors in Bayesian inference
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
This paper introduces a stochastic plug-and-play (PnP) sampling algorithm that leverages variable splitting to efficiently sample from a posterior distribution. The algorithm based…
Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows
Florentin Coeurdoux, Nicolas Dobigeon, Pierre Chainais
Optimal transport (OT) provides effective tools for comparing and mapping probability measures. We propose to leverage the flexibility of neural networks to learn an approximate op…
Robust Fusion of Multi-Band Images with Different Spatial and Spectral Resolutions for Change Detection
Vinicius Ferraris, Nicolas Dobigeon, Qi Wei +1
Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. However, in some specific cases such as emergency situations,…
Detecting Changes Between Optical Images of Different Spatial and Spectral Resolutions: a Fusion-Based Approach
Vinicius Ferraris, Nicolas Dobigeon, Qi Wei +1
Change detection is one of the most challenging issues when analyzing remotely sensed images. Comparing several multi-date images acquired through the same kind of sensor is the mo…
Fast Hyperspectral Unmixing in Presence of Nonlinearity or Mismodelling Effects
Abderrahim Halimi, Jose Bioucas-Dias, Nicolas Dobigeon +2
This paper presents two novel hyperspectral mixture models and associated unmixing algorithms. The two models assume a linear mixing model corrupted by an additive term whose expre…