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R3GAN: Simplifying GAN Training by loxadav124 is a document available to read on EtoBox.

The paper presents a modern baseline for Generative Adversarial Networks (GANs) called R3GAN, which utilizes a well-behaved regularized relativistic GAN loss to improve training stability and eliminate the need for ad-hoc tricks. By integrating contemporary architectures and simplifying existing models like StyleGAN2, R3GAN achieves superior performance on various datasets, surpassing traditional GANs and diffusion models. The authors provide mathematical analysis and empirical evidence supporting the effec

Author

loxadav124

Language

EN