Generative Adversarial Network Performance in Low-Dimensional Settings
Felix Jimenez1,2, Amanda Koepke1, Mary Gregg1
1National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.
Summary
Generative adversarial networks (GANs) show errors like tail underfilling and bridge bias in low dimensions. Understanding these errors helps improve GAN performance in simpler settings.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Deep Learning
Background:
- Generative adversarial networks (GANs) excel in high-dimensional data like images.
- GANs' behavior in low-dimensional settings is less understood.
- Low dimensions offer opportunities to identify and analyze GAN properties.
Purpose of the Study:
- To investigate GAN performance in simulated low-dimensional environments.
- To transparently assess how target distribution complexity and data size affect GANs.
- To identify and characterize specific errors in low-dimensional GANs.
Main Methods:
- Simulated low-dimensional settings were used to study GANs.
- Controlled experiments assessed the impact of distribution complexity.
- The influence of training data sample size was evaluated.
Main Results:
- Two key GAN errors were identified: tail underfilling and bridge bias.
- Bridge bias in low dimensions is analogous to tunneling in high-dimensional GANs.
- GAN performance is sensitive to distribution complexity and data sample size.
Conclusions:
- Low-dimensional studies are valuable for understanding fundamental GAN properties.
- Tail underfilling and bridge bias are critical error modes in low-dimensional GANs.
- Findings provide insights for improving GANs in various applications.
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