arXiv:2509.24935v2 Announce Type: replace-cross 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 discriminators. Training in latent space enables efficient computation while preserving perceptual fidelity, and this efficiency pa
Artificial intelligence research continues to push the boundaries of generative models, with current efforts focusing on improving scalability and efficiency, which transformer architectures are proving adept at.
Scalable Generative Adversarial Networks (GANs) using transformers could lead to more powerful and efficient AI systems for content creation, data synthesis, and complex problem-solving.
The ability to train more scalable GANs with transformer architectures significantly enhances the potential for higher fidelity and more diverse generative AI outputs, impacting various applications.
- · AI researchers
- · Creative industries
- · Data scientists
- · Compute hardware providers
- · AI models without scalable generative capabilities
- · Inefficient generative model architectures
More sophisticated and larger scale generative AI models become feasible and widespread.
This could accelerate the development of autonomous AI systems capable of complex decision-making and content generation.
The increased fidelity and realism of generated content may further blur the lines between real and synthetic information, impacting media and digital trust.
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Read at arXiv cs.LG