Learning variational autoencoder (VAE) by reading and analyzing the paper: “Auto-Encoding Variational Bayes”. This post will introduce the basic work of VAE, including the derivation of formulas and simple code verification.
This paper introduces a new generative model where samples are produced via Langevin dynamics using gradients of the data distribution estimated with score matching. And it is important to learn Score-Based generative network and Itô diffusion SDE.
Image super-resolution with conditional diffusion model.
Image deblurring with “predict-and-refine” conditional diffusion model. A brand new strategy for ill-posed problems.
Learning diffusion probabilistic models (DPM) by reading and analyzing the papers: “Deep Unsupervised Learning using Nonequilibrium Thermodynamics” and “Denoising Diffusion Probabilistic Models”. This post will introduce the basic work of DPM, including the derivation of formulas and simple code verification.