Search Results for author:
Found 5 papers, 2 papers with code
Score-based Generative Neural Networks for Large-Scale Optimal Transport
We consider the fundamental problem of sampling the optimal transport coupling between given source and target distributions.
Generator Surgery for Compressed Sensing
We introduce a method for achieving low representation error using generators as signal priors.
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder
In this paper, we demonstrate a method for reducing the representation error of GAN priors by modeling images as the linear combination of a GAN prior with a Deep Decoder.
Removing the Representation Error of GAN Image Priors Using the Deep Decoder
In this paper, we demonstrate a method for removing the representation error of a GAN when used as a prior in inverse problems by modeling images as the linear combination of a GAN with a Deep Decoder.
Invertible generative models for inverse problems: mitigating representation error and dataset bias
For compressive sensing, invertible priors can yield higher accuracy than sparsity priors across almost all undersampling ratios, and due to their lack of representation error, invertible priors can yield better reconstructions than GAN priors for images that have rare features of variation within the biased training set, including out-of-distribution natural images.
