Generalization bounds for a generator-regularized InfoGAN-inspired adversarial objective.

Area of Science:

• Machine Learning
• Deep Learning
• Generative Models

Background:

• Information Maximizing Generative Adversarial Networks (InfoGAN) offer strong empirical results but lack rigorous generalization guarantees.
• Existing InfoGAN frameworks often involve complex latent code components, hindering theoretical analysis.

Purpose of the Study:

• To develop and analyze a simplified InfoGAN-inspired adversarial framework with explicit generator regularization.
• To establish theoretical generalization error bounds for this new framework.
• To investigate the impact of generator regularization on model stability and performance.

Main Methods:

• Formulated a generator-regularized adversarial objective by removing latent codes and adding generator regularization.
• Employed Rademacher complexity to analyze the generalization gap between empirical and population objective functions.
• Derived explicit generalization error bounds with respect to sample sizes (n and m).
• Specialized theoretical analysis to two-layer neural networks with specific activation functions.

Main Results:

• Established explicit n^{-1/2} and m^{-1/2} decay rates for generalization error.
• Clarified the role and impact of the generator regularization parameter.
• Derived entropy-based complexity bounds for two-layer neural networks.
• Empirical validation on CIFAR-10 confirmed predicted scaling behavior and the stabilizing effect of generator regularization.

Conclusions:

• The proposed generator-regularized adversarial framework offers improved generalization capabilities.
• This work provides a foundational theoretical analysis for InfoGAN-inspired models with explicit generator regularization.
• Generator regularization is shown to be a key factor in enhancing the stability and generalization of adversarial learning.