Author

Ségolène Martin

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#machine learning Preprint Sep 2026

Principled MAP estimation for inverse problems: bridging the gap between convergence and performance

Pretrained denoisers provide a powerful way to incorporate image priors into restoration algorithms. Plug-and-Play and RED approaches exploit fixed-noise-level denoisers within first-order optimization schemes, with convergence guarantees, but often struggle to achieve high-quality reconstruction on severely ill-posed...

Alexandre Lagier, Valentine Tosel, Anne Gagneux et al. · 0 citations

#machine learning Preprint Sep 2026

Beyond the Manifold Hypothesis: Hybrid Spectral Parameterizations for Flow Matching

Flow matching and diffusion can be trained to predict different quantities, most commonly the data $x_1$, the source noise $x_0$, or the velocity~$v$. Although theoretically equivalent, these can lead to substantially different performances. We identify two main drivers for these differences: the source--data signal-to...

Ségolène Martin, Anne Gagneux, Quentin Bertrand et al. · 0 citations