Generative Model

Generative models are artificial intelligence systems designed to create new data instances that resemble a training dataset, aiming to learn and replicate the underlying data distribution. Current research emphasizes improving efficiency and controllability, focusing on architectures like diffusion models, autoregressive models, and generative flow networks, as well as refining training algorithms and loss functions. These advancements have significant implications across diverse fields, enabling applications such as realistic image and music generation, protein design, and improved data augmentation techniques for various machine learning tasks.