1 citations · 1 across the 5 of their papers we have counts for
5 papers
Learning to reconstruct from saturated data: audio declipping and high-dynamic range imaging
Victor Sechaud, Laurent Jacques, Patrice Abry +1
Learning based methods are now ubiquitous for solving inverse problems, but their deployment in real-world applications is often hindered by the lack of ground truth references for…
Equivariant Splitting: Self-supervised learning from incomplete data
Victor Sechaud, Jérémy Scanvic, Quentin Barthélemy +2
Self-supervised learning for inverse problems allows to train a reconstruction network from noise and/or incomplete data alone. These methods have the potential of enabling learnin…
Self-supervised learning for phase retrieval
Victor Sechaud, Patrice Abry, Laurent Jacques +1
In recent years, deep neural networks have emerged as a solution for inverse imaging problems. These networks are generally trained using pairs of images: one degraded and the othe…
DeepInverse: A Python package for solving imaging inverse problems with deep learning
Julián Tachella, Matthieu Terris, Samuel Hurault +24
DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems. The library covers all crucial steps in image reconstruction from the efficient implementa…
Equivariance-based self-supervised learning for audio signal recovery from clipped measurements
Victor Sechaud, Laurent Jacques, Patrice Abry +1
In numerous inverse problems, state-of-the-art solving strategies involve training neural networks from ground truth and associated measurement datasets that, however, may be expen…