Noiseless Diffusion-GAN: Scaling-based data augmentation for generative models.

Area of Science:

• Machine Learning
• Generative Models
• Deep Learning

Background:

• Generative models aim for high-quality data generation, often using noise injection for stable learning.
• Selecting appropriate noise distributions for stability remains a challenge.
• Diffusion-GAN utilizes diffusion processes and timestep-dependent discriminators to address stability.

Purpose of the Study:

• To analyze Diffusion-GAN and identify critical factors for stable learning and high-quality data generation.
• To introduce a novel learning algorithm, Scale-GAN, incorporating data scaling and variance-based regularization.
• To provide theoretical validation for the effectiveness of data scaling in managing the bias-variance trade-off.

Main Methods:

• Analysis of Diffusion-GAN's learning dynamics.
• Development of the Scale-GAN algorithm featuring data scaling and variance-based regularization.
• Theoretical proof of data scaling's impact on the bias-variance trade-off within estimation error bounds.

Main Results:

• Data scaling identified as a critical factor for stable learning and high-quality generation in Diffusion-GAN.
• Scale-GAN demonstrates enhanced stability and accuracy in experimental evaluations.
• Theoretical proof confirms data scaling effectively manages the bias-variance trade-off.

Conclusions:

• Data scaling is a crucial component for stable and effective generative model training.
• Scale-GAN offers an improved approach for high-quality data generation.
• The findings provide theoretical and empirical evidence for the benefits of data scaling in generative adversarial networks.