Description: Scalable GANs, or Scalable Generative Adversarial Networks, are an artificial intelligence architecture designed to optimize the data generation process as the amount of data and available computational resources increase. These networks consist of two main components: a generator and a discriminator, which compete against each other to improve the quality of the generated data. Unlike traditional GANs, which can face difficulties when scaling, scalable GANs implement techniques that allow for better resource utilization, such as parallelization and modularity. This means they can handle larger and more complex datasets without losing efficiency or quality in generating images, audio, or text. The scalable architecture allows the model to adapt to different hardware configurations, making it more accessible to researchers and developers. Additionally, its modular design facilitates the incorporation of new techniques and improvements, contributing to its ongoing evolution in the field of deep learning. In summary, scalable GANs represent a significant advancement in synthetic data generation, enabling researchers and companies to make the most of their computational resources and available data.
