The content delves into the tutorial on diffusion models for imaging and vision, focusing on Variational Auto-Encoder (VAE) and Denoising Diffusion Probabilistic Model (DDPM). It covers the basics of VAE, including VAE setting, evidence lower bound, training VAE, loss function, inference with VAE, and more. Additionally, it explores DDPM, discussing building blocks, magical scalars √αt and 1 − αt, distribution qϕ(xt|x0), and the evidence lower bound for DDPM. The tutorial provides insights into the core concepts and applications of diffusion models in the field of imaging and vision.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Stanley H. C... at arxiv.org 03-28-2024
https://arxiv.org/pdf/2403.18103.pdfDeeper Inquiries