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Selected filter tags (click to remove): Maciej-Wiatrak
2019
Maciej Wiatrak, Stefano V. Albrecht, Andrew Nystrom
Stabilizing Generative Adversarial Networks: A Survey
arXiv:1910.00927, 2019
Abstract | BibTex | arXiv
surveysecuritygan
Abstract:
Generative Adversarial Networks (GANs) are a type of generative model which have received much attention due to their ability to model complex real-world data. Despite their recent successes, the process of training GANs remains challenging, suffering from instability problems such as non-convergence, vanishing or exploding gradients, and mode collapse. In recent years, a diverse set of approaches have been proposed which focus on stabilizing the GAN training procedure. The purpose of this survey is to provide a comprehensive overview of the GAN training stabilization methods which can be found in the literature. We discuss the advantages and disadvantages of each approach, offer a comparative summary, and conclude with a discussion of open problems.
@misc{wiatrak2019stabilizing,
title={Stabilizing Generative Adversarial Networks: A Survey},
author={Maciej Wiatrak and Stefano V. Albrecht and Andrew Nystrom},
year={2019},
eprint={1910.00927},
archivePrefix={arXiv},
primaryClass={cs.LG}
}