Search Results for author:
Found 18 papers, 11 papers with code
Diffusion Posterior Sampling is Computationally Intractable
Diffusion models are a remarkably effective way of learning and sampling from a distribution $p(x)$.
PerMod: Perceptually Grounded Voice Modification with Latent Diffusion Models
Perceptual modification of voice is an elusive goal.
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
Diffusion-based generative models have been used as powerful priors for magnetic resonance imaging (MRI) reconstruction.
MRI Reconstruction with Side Information using Diffusion Models
This leads to an improvement in image reconstruction fidelity over generative models that rely only on a marginal prior over the image contrast of interest.
Accelerated Motion Correction with Deep Generative Diffusion Models
In this work we propose a framework for jointly reconstructing highly sub-sampled MRI data while estimating patient motion using diffusion based generative models.
Robust Compressed Sensing MR Imaging with Deep Generative Priors
The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems.
Robust Compressed Sensing MRI with Deep Generative Priors
The CSGM framework (Bora-Jalal-Price-Dimakis'17) has shown that deep generative priors can be powerful tools for solving inverse problems.
Fairness for Image Generation with Uncertain Sensitive Attributes
This motivates the introduction of definitions that allow algorithms to be \emph{oblivious} to the relevant groupings.
Instance-Optimal Compressed Sensing via Posterior Sampling
We characterize the measurement complexity of compressed sensing of signals drawn from a known prior distribution, even when the support of the prior is the entire space (rather than, say, sparse vectors).
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
We propose Intermediate Layer Optimization (ILO), a novel optimization algorithm for solving inverse problems with deep generative models.
Compressed Sensing with Approximate Priors via Conditional Resampling
We characterize the measurement complexity of compressed sensing of signals drawn from a known prior distribution, even when the support of the prior is the entire space (rather than, say, sparse vectors).
High Dimensional Channel Estimation Using Deep Generative Networks
This paper presents a novel compressed sensing (CS) approach to high dimensional wireless channel estimation by optimizing the input to a deep generative network.
Robust Compressed Sensing using Generative Models
In analogy to classical compressed sensing, here we assume a generative model as a prior, that is, we assume the vector is represented by a deep generative model $G: \mathbb{R}^k \rightarrow \mathbb{R}^n$.
Deep Learning Techniques for Inverse Problems in Imaging
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.
Inverting Deep Generative models, One layer at a time
For generative models of arbitrary depth, we show that exact recovery is possible in polynomial time with high probability, if the layers are expanding and the weights are randomly selected.
Compressed Sensing with Deep Image Prior and Learned Regularization
We propose a novel method for compressed sensing recovery using untrained deep generative models.
The Robust Manifold Defense: Adversarial Training using Generative Models
Our formulation involves solving a min-max problem, where the min player sets the parameters of the classifier and the max player is running our attack, and is thus searching for adversarial examples in the {\em low-dimensional} input space of the spanner.
Compressed Sensing using Generative Models
The goal of compressed sensing is to estimate a vector from an underdetermined system of noisy linear measurements, by making use of prior knowledge on the structure of vectors in the relevant domain.
