Conditional Generative

Conditional generative modeling focuses on creating new data instances conditioned on specific inputs, aiming to learn complex conditional distributions and generate high-fidelity samples. Current research emphasizes diverse model architectures, including diffusion models, energy-based models, and normalizing flows, often applied to tasks like image generation, video prediction, and scientific simulation. These advancements are significantly impacting various fields, enabling data augmentation for improved classification, realistic simulations in physics and medicine, and the development of more powerful AI tools for diverse applications.