# Solving Inverse Problems by Joint Posterior Maximization with a VAE Prior

@article{Gonzalez2019SolvingIP, title={Solving Inverse Problems by Joint Posterior Maximization with a VAE Prior}, author={Mario Gonz'alez and Andr{\'e}s Almansa and Mauricio Delbracio and Pablo Mus'e and Pauline Tan}, journal={ArXiv}, year={2019}, volume={abs/1911.06379} }

In this paper we address the problem of solving ill-posed inverse problems in imaging where the prior is a neural generative model. Specifically we consider the decoupled case where the prior is trained once and can be reused for many different log-concave degradation models without retraining. Whereas previous MAP-based approaches to this problem lead to highly non-convex optimization algorithms, our approach computes the joint (space-latent) MAP that naturally leads to alternate optimization… Expand

#### 5 Citations

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