  Incorporating a deep generative model as the prior distribution in inverse
problems has established substantial success in reconstructing images from
corrupted observations. Notwithstanding, the existing optimization approaches
use gradient descent largely without adapting to the non-convex nature of the
problem and can be sensitive to initial values, impeding further performance
improvement. In this paper, we propose an efficient amortized optimization
scheme for inverse problems with a deep generative prior. Specifically, the
optimization task with high degrees of difficulty is decomposed into optimizing
a sequence of much easier ones. We provide a theoretical guarantee of the
proposed algorithm and empirically validate it on different inverse problems.
As a result, our approach outperforms baseline methods qualitatively and
quantitatively by a large margin.
