Scalable GANs with Transformers 事件

Scalable GANs with Transformers arXiv:2509.24935v2 Announce Type: replace Abstract: Scalability has driven recent advances in generative modeling, yet its principles remain underexplored for adversarial learning. We investigate the scalability of Generative Adversarial Networks (GANs) through two design choices that have proven to be effective in other types of generative models: training in a compact Variational Autoencoder latent space and adopting purely transformer-based generators and disc