Hierarchical Generative

Hierarchical generative models aim to create complex data structures by building them up from simpler components in a layered fashion, mirroring the hierarchical nature of many real-world phenomena. Current research focuses on developing and improving these models using architectures like transformers, U-Nets, and normalizing flows, often incorporating techniques such as variational inference and diffusion processes to enhance efficiency and performance. This approach is proving valuable in diverse fields, including 3D scene generation, language modeling, molecular design, and robotic control, by enabling the generation of more realistic and complex data than previously possible.