Skip to content
All tags

#generative-model

2 posts
tech MIT 6.7960 導讀 (Fall 2024 OCW)

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'.

#mit-67960#deep-learning#generative-model#gan#diffusion#autoregressive#fall-2024
tech MIT 6.7960 導讀 (Fall 2024 OCW)

MIT 6.7960 L15: Variational Autoencoders (VAE) — ELBO, Reparameterization Trick, and Latent Representations

The core of VAE is ELBO + reparameterization: log p(x) is replaced with E_q[log p(x|z)] − KL(q(z|x)‖p(z)); the encoder outputs μ/σ and z = μ + σ⊙ε (ε ~ N(0,1)) makes sampling differentiable. Training = reconstruction + KL in tension, which gives rise to β-VAE, posterior collapse, VQ-VAE, and related fixes.

#mit-67960#deep-learning#vae#variational-autoencoder#elbo#generative-model#fall-2024