Deep Learning

A Brief Exploration to Variational Autoencoder (VAE) with Code Implementation

Learn 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.

[NeurIPS'19 Oral] Generative Modeling by Estimating Gradients of the Data Distribution 阅读报告

This paper introduce 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 Ito diffusion SDE.

[T-PAMI'23] Image Super-Resolution via Iterative Refinement 阅读报告

Image super-resolution with conditional diffusion model.

[CVPR'22] Deblurring via Stochastic Refinement 阅读报告

Image deblurring with “predict-and-refine” conditional diffusion model. An brand new strategy for ill-posed problem.

A Brief Exploration to Diffusion Probabilistic Models with Code Implementation

Learn 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.