Error analysis of generative adversarial network
This provides a theoretical foundation for error analysis in GANs, addressing a pressing need in high-dimensional distribution learning, but it is incremental as it builds on existing error estimation methods.
The paper tackles the problem of understanding the error convergence rate of Generative Adversarial Networks (GANs) by establishing a tight convergence rate using the Talagrand inequality and Borel-Cantelli lemma, which can improve existing error estimations.
The generative adversarial network (GAN) is an important model developed for high-dimensional distribution learning in recent years. However, there is a pressing need for a comprehensive method to understand its error convergence rate. In this research, we focus on studying the error convergence rate of the GAN model that is based on a class of functions encompassing the discriminator and generator neural networks. These functions are VC type with bounded envelope function under our assumptions, enabling the application of the Talagrand inequality. By employing the Talagrand inequality and Borel-Cantelli lemma, we establish a tight convergence rate for the error of GAN. This method can also be applied on existing error estimations of GAN and yields improved convergence rates. In particular, the error defined with the neural network distance is a special case error in our definition.