26 citations · 49 across the 6 of their papers we have counts for
6 papers
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.…
Barycenters of Natural Images -- Constrained Wasserstein Barycenters for Image Morphing
Dror Simon, Aviad Aberdam
Image interpolation, or image morphing, refers to a visual transition between two (or more) input images. For such a transition to look visually appealing, its desirable properties…
Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles
Dror Simon, Miriam Farber, Roman Goldenberg
Auto-annotation by ensemble of models is an efficient method of learning on unlabeled data. Wrong or inaccurate annotations generated by the ensemble may lead to performance degrad…
Rethinking the CSC Model for Natural Images
Dror Simon, Michael Elad
Sparse representation with respect to an overcomplete dictionary is often used when regularizing inverse problems in signal and image processing. In recent years, the Convolutional…
MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance
Dror Simon, Jeremias Sulam, Yaniv Romano +2
Sparse coding refers to the pursuit of the sparsest representation of a signal in a typically overcomplete dictionary. From a Bayesian perspective, sparse coding provides a Maximum…