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Found 3 papers, 2 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.
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.
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