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Found 3 papers, 1 papers with code
Wasserstein Generative Learning of Conditional Distribution
The proposed approach uses a conditional generator to transform a known distribution to the target conditional distribution.
Non-Asymptotic Error Bounds for Bidirectional GANs
Our results are also applicable to the Wasserstein bidirectional GAN if the target distribution is assumed to have a bounded support.
An error analysis of generative adversarial networks for learning distributions
This paper studies how well generative adversarial networks (GANs) learn probability distributions from finite samples.
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