740 citations · 3.4k across the 125 of their papers we have counts for
9 papers · 1 filter
Deblurring via Stochastic Refinement
Jay Whang, Mauricio Delbracio, Hossein Talebi +3
Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean ima…
Solving Inverse Problems with NerfGANs
Giannis Daras, Wen-Sheng Chu, Abhishek Kumar +2
We introduce a novel framework for solving inverse problems using NeRF-style generative models. We are interested in the problem of 3-D scene reconstruction given a single 2-D imag…
Inverse Problems Leveraging Pre-trained Contrastive Representations
Sriram Ravula, Georgios Smyrnis, Matt Jordan +1
We study a new family of inverse problems for recovering representations of corrupted data. We assume access to a pre-trained representation learning network R(x) that operates on…
Robust Compressed Sensing MRI with Deep Generative Priors
Ajil Jalal, Marius Arvinte, Giannis Daras +3
The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems. However, to date this framework has been…
Provable Lipschitz Certification for Generative Models
Matt Jordan, Alexandros G. Dimakis
We present a scalable technique for upper bounding the Lipschitz constant of generative models. We relate this quantity to the maximal norm over the set of attainable vector-Jacobi…
Fairness for Image Generation with Uncertain Sensitive Attributes
Ajil Jalal, Sushrut Karmalkar, Jessica Hoffmann +2
This work tackles the issue of fairness in the context of generative procedures, such as image super-resolution, which entail different definitions from the standard classification…