GAN已死；GAN永存！一个现代GAN基准

The GAN is dead; long live the GAN! A Modern GAN Baseline

January 9, 2025

作者: Yiwen Huang, Aaron Gokaslan, Volodymyr Kuleshov, James Tompkin

cs.AI

摘要

有一种广泛流传的说法认为生成对抗网络（GANs）难以训练，文献中的GAN架构充斥着经验性技巧。我们提供证据反驳这一说法，并以更加原则性的方式构建了现代GAN基准线。首先，我们推导出一个行为良好的正则化相对论GAN损失，解决了以往通过一堆临时技巧来应对的模式丢失和不收敛问题。我们对我们的损失进行数学分析，并证明它具有局部收敛保证，这与大多数现有的相对论损失不同。其次，我们的新损失使我们能够放弃所有临时技巧，并用现代架构替换常见GAN中使用的过时骨干结构。以StyleGAN2为例，我们提出了一种简化和现代化的路线图，形成了一种新的极简基准线——R3GAN。尽管简单，我们的方法在FFHQ、ImageNet、CIFAR和Stacked MNIST数据集上均超越了StyleGAN2，并且与最先进的GAN和扩散模型相比表现优异。

English

There is a widely-spread claim that GANs are difficult to train, and GAN architectures in the literature are littered with empirical tricks. We provide evidence against this claim and build a modern GAN baseline in a more principled manner. First, we derive a well-behaved regularized relativistic GAN loss that addresses issues of mode dropping and non-convergence that were previously tackled via a bag of ad-hoc tricks. We analyze our loss mathematically and prove that it admits local convergence guarantees, unlike most existing relativistic losses. Second, our new loss allows us to discard all ad-hoc tricks and replace outdated backbones used in common GANs with modern architectures. Using StyleGAN2 as an example, we present a roadmap of simplification and modernization that results in a new minimalist baseline -- R3GAN. Despite being simple, our approach surpasses StyleGAN2 on FFHQ, ImageNet, CIFAR, and Stacked MNIST datasets, and compares favorably against state-of-the-art GANs and diffusion models.