Generative Adversarial Network: Some Analytical Perspectives
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Abstract (arXiv)
Ever since its debut, generative adversarial networks (GANs) have attracted tremendous amount of attention. Over the past years, different variations of GANs models have been developed and tailored to different applications in practice. Meanwhile, some issues regarding the performance and training of GANs have been noticed and investigated from various theoretical perspectives. This subchapter will start from an introduction of GANs from an analytical perspective, then move on to the training of GANs via SDE approximations and finally discuss some applications of GANs in computing high dimensional MFGs as well as tackling mathematical finance problems.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 33 of its 49 references that are in the map. First posted on arXiv 25 Apr 2021.
Builds on
33 of 49 references.
References · 33
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21 more
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Citing papers per year: 2021: 1; 2022: 1.
2 papers.