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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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11030454/ |
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