Hierarchical Deep Generative

Hierarchical deep generative models aim to create complex data by building up from simpler representations, capturing intricate relationships within hierarchical structures. Current research focuses on developing these models for diverse applications, including audio synthesis, image manipulation (like face morphing and super-resolution), and urban planning, often employing generative adversarial networks (GANs) or other modular architectures with multi-objective loss functions. This approach allows for the generation of more realistic and nuanced data, improving the performance of downstream tasks and offering valuable tools for various fields, from engineering design to medical signal processing.