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Found 3 papers, 3 papers with code
A variable metric proximal stochastic gradient method: an application to classification problems
To control the variance of the objective's gradients, we use an automatic sample size selection along with a variable metric to precondition the stochastic gradient directions.
Constrained and unconstrained deep image prior optimization models with automatic regularization
Numerical results demonstrate the robustness with respect to image content, noise levels and hyperparameters of the proposed models on both denoising and deblurring of simulated as well as real natural and medical images.
Combining Weighted Total Variation and Deep Image Prior for natural and medical image restoration via ADMM
In the last decades, unsupervised deep learning based methods have caught researchers attention, since in many real applications, such as medical imaging, collecting a great amount of training examples is not always feasible.
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