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Found 3 papers, 2 papers with code
MCGAN: Enhancing GAN Training with Regression-Based Generator Loss
This approach, utilizing an innovative generative loss function, termly the regression loss, reformulates the generator training as a regression task and enables the generator training by minimizing the mean squared error between the discriminator's output of real data and the expected discriminator of fake data.
Sig-Wasserstein GANs for Time Series Generation
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines.
Conditional Sig-Wasserstein GANs for Time Series Generation
The signature of a path is a graded sequence of statistics that provides a universal description for a stream of data, and its expected value characterises the law of the time-series model.
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