128 citations · 155 across the 10 of their papers we have counts for
7 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…
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.…
Ada-LISTA: Learned Solvers Adaptive to Varying Models
Aviad Aberdam, Alona Golts, Michael Elad
Neural networks that are based on unfolding of an iterative solver, such as LISTA (learned iterative soft threshold algorithm), are widely used due to their accelerated performance…
Deep K-SVD Denoising
Meyer Scetbon, Michael Elad, Peyman Milanfar
This work considers noise removal from images, focusing on the well known K-SVD denoising algorithm. This sparsity-based method was proposed in 2006, and for a short while it was c…
Finding GEMS: Multi-Scale Dictionaries for High-Dimensional Graph Signals
Yael Yankelevsky, Michael Elad
Modern data introduces new challenges to classic signal processing approaches, leading to a growing interest in the field of graph signal processing. A powerful and well establishe…
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks
Jeremias Sulam, Aviad Aberdam, Amir Beck +1
Parsimonious representations are ubiquitous in modeling and processing information. Motivated by the recent Multi-Layer Convolutional Sparse Coding (ML-CSC) model, we herein genera…