Self-Adaptive GAN Training Strategy

Abstract

This paper introduces a novel self-adaptive GAN (GAN) training strategy designed to enhance model generation quality and stability. Traditional GAN training often relies on manual parameter adjustment, which can be time-consuming and suboptimal. Our approach leverages a dynamic adjustment mechanism to automatically tune the generator and discriminator parameters based on the training data. We propose a method for iteratively optimizing these parameters using a reinforcement learning framework, ensuring a smoother convergence and improved sample quality. The proposed strategy demonstrates significant improvements in generating diversity and fidelity compared to existing methods, offering a more efficient and robust training process.