A Regularized Conditional GAN for Posterior Sampling in Image Recovery Problems
Matthew C Bendel, R. Badlishah Ahmad, Philip Schniter · PubMed · 2022
penalty and an adaptively weighted standard-deviation reward. Using quantitative evaluation metrics like conditional Fréchet inception distance, we demonstrate that our method produces state-of-the-art posterior samples in both multicoil MRI and large-scale inpainting applications. The code for our model can be found here: https://github.com/matt-bendel/rcGAN.