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Generative Adversarial Network Performance in Low-Dimensional Settings.

Felix Jimenez^1,2, Amanda Koepke¹, Mary Gregg¹

¹National Institute of Standards and Technology, Gaithersburg, MD 20899, USA.

Journal of Research of the National Institute of Standards and Technology

|July 17, 2024

Summary

This summary is machine-generated.

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.

Keywords:

earth mover distance experiment protocol generative adversarial network mode tunneling modeling error target distribution complexity

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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.