Randomized trial demonstrates improved generation quality and training stability in GAN architectures, indicating the value of adaptive Lipschitz constraints.
Key Points
This work aims to improve training stability in Generative Adversarial Networks by introducing adaptive Lipschitz constraints.
Developed a theoretical framework for adaptive Lipschitz constraint control based on architecture and training state.
Introduced Adaptive Lipschitz Constraint (ALC) to adjust the Lipschitz constant dynamically during training.
Conducted experiments across multiple GAN architectures to evaluate stability and quality.
Demonstrated that ALC significantly improves generation quality compared to conventional fixed-constraint methods.
Observed enhanced training stability across tested GAN architectures.
Results indicate that adaptive control of the Lipschitz constraint positively influences model performance.