The GAN is dead; long live the GAN! A Modern GAN Baseline
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all ad-hoc tricks and replace outdated backbones used in common GANs with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.
6780827489ff720d2e028eb8
Yiwen Huang
admin_assigned
2025-01-10T08:25:22.968Z
656988c35958c68e3180961e
/avatars/7e7916a9c9d2502774dd7727c1a03049.svg
Yiwen Huang
user
Eva1209
6780827489ff720d2e028eb9
Aaron Gokaslan
admin_assigned
2025-01-10T08:25:29.960Z
620573d0522e40b4a18d8763
/avatars/9353c064ef8ccac84d0397411d38fa90.svg
Aaron Gokaslan
user
Skylion007
6780827489ff720d2e028eba
Volodymyr Kuleshov
admin_assigned
2025-01-10T08:25:37.188Z
640ea1c43c82bd463ee80e19
/avatars/3213d2e8a433a207dfb96a35f0f52a92.svg
Volodymyr Kuleshov
user
kuleshov
6780827489ff720d2e028ebb
James Tompkin
admin_assigned
2025-01-10T08:25:45.080Z
6581b07b4466994ea8491941
https://cdn-avatars.huggingface.co/v1/production/uploads/6581b07b4466994ea8491941/dsKpoHwnyTqCaSRMV3evn.png
James Tompkin
user
jamestompkin
The GAN is dead; long live the GAN! A Modern GAN Baseline
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.
A new regularization approach and simplified architecture for GANs, named R3GAN, surpasses existing methods including StyleGAN2 and diffusion models across multiple datasets.
GANs relativistic GAN loss mode dropping non-convergence StyleGAN2 R3GAN FFHQ ImageNet CIFAR Stacked MNIST