This paper studies stable learning methods for generative models that enable high-quality data generation. Noise injection is commonly used to stabilize learning. However, selecting a suitable noise distribution is challenging. Diffusion-GAN, a recently developed method, addresses this by using the diffusion process with a timestep-dependent discriminator. We investigate Diffusion-GAN and reveal that data scaling is a key component for stable learning and high-quality data generation. Building on our findings, we propose a learning algorithm, Scale-GAN, that uses data scaling and variance-based regularization. Furthermore, we theoretically prove that data scaling controls the bias-variance trade-off of the estimation error bound. As a theoretical extension, we consider GAN with invertible data augmentations. Comparative evaluations on benchmark datasets demonstrate the effectiveness of our method in improving stability and accuracy.

翻译：本文研究生成模型的稳定学习方法，以实现高质量数据生成。噪声注入是常用的学习稳定化技术，但选择合适的噪声分布具有挑战性。近期提出的Diffusion-GAN方法通过采用扩散过程和时变判别器解决了这一问题。我们深入分析Diffusion-GAN，发现数据缩放是实现稳定学习和高质量数据生成的关键要素。基于此发现，我们提出一种结合数据缩放与方差正则化的学习算法Scale-GAN。进一步，我们通过理论证明数据缩放能够控制估计误差界的偏差-方差权衡。作为理论扩展，我们研究了具有可逆数据增强的GAN模型。在基准数据集上的对比实验表明，本方法能有效提升训练稳定性与生成精度。