个人笔记目录

目录

生成模型笔记

• 生成模型笔记（一）：概率基础知识

• 生成模型笔记（二）：最大似然，贝叶斯和最大后验概率

• 生成模型笔记（三）：近似推断

• 生成模型笔记（四）：MCMC和Gibbs Sampling

• 生成模型笔记（五）：判别模型

• 生成模型笔记（六）：生成模型

• 生成模型笔记（七）：自回归模型

• 生成模型笔记（八）：Flow-Based Models

最优传输笔记

pytorch笔记

文章阅读记录

Diffusion Models

• 001_SSSSS_ Denoising Diffusion Probabilistic Models

• 002_SSSS_ Denoising Diffusion Implicit Models

• 003_SSS_ Tackling the Generative Learning Trilemma with Denoising Diffusion GANs

• 005_SS_ Palette Image-to-Image Diffusion Models

• 006_SS_ Dual Diffusion Implicit Bridges For Image-to-Image Translation

• 008_SSSS_ Improved Denoising Diffusion Probabilistic Models

• 012_SSS_ Improving Diffusion Model Efficiency Through Patching

• 013_SSS_ Frido Feature Pyramid Diffusion for Complex Scene Image Synthesis

• 014_SSS_ High-Resolution Image Synthesis with Latent Diffusion Models

• 017_SSS_Semantic Image Synthesis via Diffusion Models

• 018_SS_High-Resolution Image Editing via Multi-Stage Blended Diffusion

• 021_SSSS_Diffusion Models Already Have a Semantic Latent Space

GAN

• 004_SSSS_ Image-to-Image Translation with Conditional Adversarial Networks

• 019_SSSS_High-Resolution Image Synthesis and Semantic Manipulation with Conditioanl GANs

• 020_SSSS_A Style-Based Generator Architecture for Generative Adversarial Networks(StyleGAN)

VAE

Normalizing Flow

Neural Ordinary Differential Equations

• 007_SSSSS_ Neural Ordinary Differential Equtions

• 016_SSS_ GAN Inversion for Consistent Video Interpolation and Manipulation

• 007_补充_ Pytorch 反向传播和Neural ODE的反向传播

Other Classical Papers

• 015_SSSSS_ Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

3D Papers

• 022_SSS_Novel View Synthesis with Diffusion Models

• 023_SSS_Neural 3D Video Synthesis from Multi-view Video(CVPR2022)