MIT 6.7960 L14: Generative Models Basics — Density/Energy Models, GANs, Autoregressive, Diffusion
Generative models learn the data distribution p(x). Density models model probability directly, energy models use an unnormalized potential + sampler, GANs let a discriminator force realistic samples, autoregressive predicts the next token step by step, and diffusion dodges tricky maximum-likelihood via 'add noise then learn to denoise'.