Representing a manifold of very high-dimensional data with generative mo...
Compressed sensing allows for the recovery of sparse signals from few
me...
As interest in deep neural networks (DNNs) for image reconstruction task...
In this work, we present and study Continuous Generative Neural Networks...
In this work, we consider the linear inverse problem y=Ax+ϵ, where
A X→ ...
Photoacoustic tomography (PAT) is an emerging imaging modality that aims...
While deep neural networks have proven to be a powerful tool for many
re...
We consider the dynamical super-resolution problem consisting in the rec...