Scalable Generative Model

Scalable generative models aim to create realistic and diverse synthetic data from complex, high-dimensional sources, addressing limitations of existing methods in handling large datasets and intricate structures. Current research focuses on developing efficient architectures, such as autoregressive models and diffusion models, often combined with techniques like tokenization and latent space manipulation to improve scalability and generative quality across various data types, including images, point clouds, and dynamic graphs. These advancements are significantly impacting fields like autonomous driving, medical imaging, and network analysis by enabling more robust model training, improved simulation, and enhanced data augmentation for tasks ranging from motion planning to disease prediction.