4 papers
Latent Generative Models with Tunable Complexity for Compressed Sensing and other Inverse Problems
Sean Gunn, Jorio Cocola, Oliver De Candido +2
Generative models have emerged as powerful priors for solving inverse problems. These models typically represent a class of natural signals using a single fixed complexity or dimen…
Invertible generative models for inverse problems: mitigating representation error and dataset bias
Muhammad Asim, Mara Daniels, Oscar Leong +2
Trained generative models have shown remarkable performance as priors for inverse problems in imaging -- for example, Generative Adversarial Network priors permit recovery of test…
Score-based Generative Neural Networks for Large-Scale Optimal Transport
Mara Daniels, Tyler Maunu, Paul Hand
We consider the fundamental problem of sampling the optimal transport coupling between given source and target distributions. In certain cases, the optimal transport plan takes the…
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder
Mara Daniels, Paul Hand, Reinhard Heckel
Generative models, such as GANs, learn an explicit low-dimensional representation of a particular class of images, and so they may be used as natural image priors for solving inver…