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Found 8 papers, 3 papers with code
On the Inversion of Deep Generative Models
Deep generative models (e. g. GANs and VAEs) have been developed quite extensively in recent years.
Learned Greedy Method (LGM): A Novel Neural Architecture for Sparse Coding and Beyond
A popular representative of this approach is the Iterative Shrinkage-Thresholding Algorithm (ISTA) and its learned version -- LISTA, aiming for the sparse representations of the processed signals.
When and How Can Deep Generative Models be Inverted?
Deep generative models (e. g. GANs and VAEs) have been developed quite extensively in recent years.
Barycenters of Natural Images Constrained Wasserstein Barycenters for Image Morphing
Image interpolation, or image morphing, refers to a visual transition between two (or more) input images.
Barycenters of Natural Images -- Constrained Wasserstein Barycenters for Image Morphing
Image interpolation, or image morphing, refers to a visual transition between two (or more) input images.
Auto-Annotation Quality Prediction for Semi-Supervised Learning with Ensembles
Auto-annotation by ensemble of models is an efficient method of learning on unlabeled data.
Rethinking the CSC Model for Natural Images
Sparse representation with respect to an overcomplete dictionary is often used when regularizing inverse problems in signal and image processing.
Ranked #1 on
Color Image Denoising
on BSD68 sigma75
MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance
The proposed method adds controlled noise to the input and estimates a sparse representation from the perturbed signal.
