How Good Are Deep Generative Models for Solving Inverse Problems?
Shichong Peng Alireza Moazeni Ke Li
Deep generative models, such as diffusion models, GANs, and IMLE, have shown
impressive capability in tackling inverse problems. However, the validity of
model-generated solutions w.r.t. the forward problem and the reliability of
associated uncertainty estimates remain understudied. This study evaluates
recent diffusion-based, GAN-based, and IMLE-based methods on three inverse
problems, i.e., $16\times$ super-resolution, colourization, and image
decompression. We assess the validity of these models' outputs as solutions to
the inverse problems and conduct a thorough analysis of the reliability of the
models' estimates of uncertainty over the solution. Overall, we find that the
IMLE-based CHIMLE method outperforms other methods in terms of producing valid
solutions and reliable uncertainty estimates.