Deep Generative

Deep generative models aim to learn the underlying probability distribution of data to generate new, realistic samples. Current research focuses on improving the quality and diversity of generated data, addressing challenges like mode collapse and scalability, and exploring novel architectures such as diffusion models, normalizing flows, and generative adversarial networks (GANs) applied to diverse data types (images, text, tabular data, point clouds). These advancements have significant implications across various fields, enabling applications like data augmentation, anomaly detection, and improved forecasting in areas such as power systems and medical imaging.