Generative Modeling

Generative modeling studies how to learn the structure and variability of data so a model can produce new samples, fill in missing information, or propose designs. Major approaches include variational autoencoders, generative adversarial networks, autoregressive models, normalizing flows, energy-based models, and diffusion, score-based, and flow-matching methods. Researchers evaluate these methods on tasks such as image and video synthesis, text generation, molecular design, and scientific simulation, using measures of fidelity, diversity, coverage, and downstream usefulness. Current work seeks faster, more controllable generation that respects constraints and data geometry, while addressing trade-offs among quality, diversity, reliability, compute cost, and memorization.