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Inverse Problem Regularization with a Variational Autoencoder Prior | title | Inverse Problem Regularization with a Variational Autoencoder Prior |
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| start_date | 2024/02/06 |
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| schedule | 14h-16h |
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| online | no |
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| location_info | Salle 314 |
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| summary | In this presentation, I will introduce various strategies to use pretrained variational autoencoders (VAE) as a prior model to regularize ill-posed image inverse problems, such as deblurring or super-resolution. VAE can model complex data such as images, by defining a latent variable model paramaterized by a deep neural network. However, it is difficult to use the probabilistic model learned by a VAE as a prior for an inverse problem, because it is defined as an intractable integral. In order to circumvent the intractability of the VAE model, I will first present PnP-HVAE, an iterative optimization algorithm which maximizes a joint posterior distribution on an augmented (image-latent) space. PnP-HVAE is adapted to expressive hierarchical VAE models, and enables us to control the strength of the regularization. Additionally, we draw connection with Plug-and-play methods based on deep image denoisers, and we demonstrate the convergence of our algorithm. Next, I will introduce a strategy to sample from the posterior distribution of a super-resolution problem by using a hierarchical VAE (HVAE) as a prior model. To this end, we propose to train an additional encoder on degraded observation in order to condition the HVAE generative process on the degraded observation. We demonstrate that our approach provides sample quality on par with recent diffusion models while being significantly more computationally efficient. |
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| responsibles | Vacher, Blusseau |
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| submitted | published | | 2024/02/05 13:33 UTC |
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