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Deep generative models, such as diffusion models, GANs, and IMLE, have shownimpressive capability in tackling inverse problems. However, the validity ofmodel-generated solutions w.r.t. the forward problem and the reliability ofassociated uncertainty estimates remain understudied. This study evaluatesrecent diffusion-based, GAN-based, and IMLE-based methods on three inverseproblems, i.e., $16\times$ super-resolution, colourization, and imagedecompression. We assess the validity of these models' outputs as solutions tothe inverse problems and conduct a thorough analysis of the reliability of themodels' estimates of uncertainty over the solution. Overall, we find that theIMLE-based CHIMLE method outperforms other methods in terms of producing validsolutions and reliable uncertainty estimates.

How Good Are Deep Generative Models for Solving Inverse Problems?