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Found 6 papers, 4 papers with code
Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems
In inverse problems, it is widely recognized that the incorporation of a sparsity prior yields a regularization effect on the solution.
Manifold Learning by Mixture Models of VAEs for Inverse Problems
Representing a manifold of very high-dimensional data with generative models has been shown to be computationally efficient in practice.
Localized adversarial artifacts for compressed sensing MRI
As interest in deep neural networks (DNNs) for image reconstruction tasks grows, their reliability has been called into question (Antun et al., 2020; Gottschling et al., 2020).
Continuous Generative Neural Networks
In this work, we present and study Continuous Generative Neural Networks (CGNNs), namely, generative models in the continuous setting: the output of a CGNN belongs to an infinite-dimensional function space.
Learning the optimal Tikhonov regularizer for inverse problems
Then, we consider the problem of learning the regularizer from a finite training set in two different frameworks: one supervised, based on samples of both $x$ and $y$, and one unsupervised, based only on samples of $x$.
ADef: an Iterative Algorithm to Construct Adversarial Deformations
While deep neural networks have proven to be a powerful tool for many recognition and classification tasks, their stability properties are still not well understood.
