Training deep neural networks requires gradient estimation from data batches to update
parameters. Gradients per parameter are averaged over a set of data and this has been …

Abstract The CSGM framework (Bora-Jalal-Price-Dimakis' 17) has shown that
deepgenerative priors can be powerful tools for solving inverse problems. However, to date …

GAN inversion aims to invert a given image back into the latent space of a pretrained GAN
model so that the image can be faithfully reconstructed from the inverted code by the …

Recent work in machine learning shows that deep neural networks can be used to solve a
wide variety of inverse problems arising in computational imaging. We explore the central …

Deep neural network approaches to inverse imaging problems have produced impressive
results in the last few years. In this survey paper, we consider the use of generative models …

Most 3D shape completion approaches rely heavily on partial-complete shape pairs and
learn in a fully supervised manner. Despite their impressive performances on in-domain …

Understanding when and how much a model gradient leaks information about the training
sample is an important question in privacy. In this paper, we present a surprising result …

We propose Intermediate Layer Optimization (ILO), a novel optimization algorithm for solving
inverse problems with deep generative models. Instead of optimizing only over the initial …

Image denoising is a well-known and well studied problem, commonly targeting a
minimization of the mean squared error (MSE) between the outcome and the original image …

In recent years, there have been significant advances in the use of deep learning methods in
inverse problems such as denoising, compressive sensing, inpainting, and super-resolution …