Quasi-Analytical Least-Squares Generative Adversarial Networks: Further 1-D Results and Extension to Two Data Dimensions

Quasi-Analytical Least-Squares Generative Adversarial Networks: Further 1-D Results and Extension to Two Data Dimensions

Generative adversarial networks (GANs) are notoriously difficult to analyse, necessitating empirical studies in high dimensional spaces that suffer from stochastic sampling noise. Quasi-analytical, low-dimensional GANs can be developed in various special cases to elucidate aspects of GAN training in...

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Bibliographic Details
Main Author: Graham W. Pulford
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Bifurcation
error propagation
explicit low-dimensional model
exponential mixture
generative adversarial network
loss surface
Online Access:https://ieeexplore.ieee.org/document/11030454/
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https://ieeexplore.ieee.org/document/11030454/

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