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Found 3 papers, 1 papers with code
Unsupervised Training of Convex Regularizers using Maximum Likelihood Estimation
Unsupervised learning is a training approach in the situation where ground truth data is unavailable, such as inverse imaging problems.
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems
Bayesian methods for solving inverse problems are a powerful alternative to classical methods since the Bayesian approach offers the ability to quantify the uncertainty in the solution.
TRL: Discriminative Hints for Scalable Reverse Curriculum Learning
However, tasks with sparse rewards remain challenging when the state space is large.
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