128 citations · 155 across the 10 of their papers we have counts for
8 papers · 1 filter
Learned Greedy Method (LGM): A Novel Neural Architecture for Sparse Coding and Beyond
Rajaei Khatib, Dror Simon, Michael Elad
The fields of signal and image processing have been deeply influenced by the introduction of deep neural networks. These are successfully deployed in a wide range of real-world app…
The Rate-Distortion-Accuracy Tradeoff: JPEG Case Study
Xiyang Luo, Hossein Talebi, Feng Yang +2
Handling digital images is almost always accompanied by a lossy compression in order to facilitate efficient transmission and storage. This introduces an unavoidable tension betwee…
Regularization by Denoising via Fixed-Point Projection (RED-PRO)
Regev Cohen, Michael Elad, Peyman Milanfar
Inverse problems in image processing are typically cast as optimization tasks, consisting of data-fidelity and stabilizing regularization terms. A recent regularization strategy of…
Optimization with Zeroth-Order Oracles in Formation
Elad Michael, Daniel Zelazo, Tony A. Wood +2
In this paper, we consider the optimisation of time varying functions by a network of agents with no gradient information. The proposed a novel method to estimate the gradient at e…
When and How Can Deep Generative Models be Inverted?
Aviad Aberdam, Dror Simon, Michael Elad
Deep generative models (e.g. GANs and VAEs) have been developed quite extensively in recent years. Lately, there has been an increased interest in the inversion of such a model, i.…
Uncertainty Intervals for Robust Bottleneck Assignment
Elad Michael, Tony A. Wood, Chris Manzie +1
We examine the robustness of bottleneck assignment problems to perturbations in the assignment weights. We derive two algorithms that provide uncertainty bounds for robust assignme…