Image-Adaptive GAN based Reconstruction

Understand

In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative adversarial networks.

• However, the representation capabilities of these methods still do not capture the full distribution for complex classes of images, such as human faces.
• This deficiency has been clearly observed in previous works that use pre-trained generative models to solve imaging inverse problems.
• In this paper, we suggest to mitigate the limited representation capabilities of generators by making them image-adaptive and enforcing compliance of the restoration with the observations via back-projections.