Conditional Generative Modeling

Conditional generative modeling studies how to generate plausible data given information such as labels, text, images, measurements, or physical parameters. Common approaches include conditional GANs and VAEs, normalizing flows, diffusion and score-based models, flow matching, and autoregressive or masked models. These methods support tasks such as image reconstruction, time-series forecasting, molecular design, and scientific inverse problems, where many possible outputs may fit the same input. A central challenge is balancing fidelity to the condition and output diversity with fast sampling, reliable uncertainty, and generalization to unseen conditions.