【综述】Diffusion Models: A Comprehensive Survey of Methods and Applications

Diffusion Models: A Comprehensive Survey of Methods and Applications

论文:https://arxiv.org/abs/2209.00796

github:https://github.com/YangLing0818/Diffusion-Models-Papers-Survey-Taxonomy

目录

[Diffusion Models: A Comprehensive Survey of Methods and Applications](#Diffusion Models: A Comprehensive Survey of Methods and Applications)

[Algorithm Taxonomy](#Algorithm Taxonomy)

[1. Efficient Sampling](#1. Efficient Sampling)

[1.1 Learning-Free Sampling](#1.1 Learning-Free Sampling)

[1.1.1 SDE Solver](#1.1.1 SDE Solver)

[1.1.2 ODE Solver](#1.1.2 ODE Solver)

[1.2 Learning-Based Sampling](#1.2 Learning-Based Sampling)

[1.2.1 Optimized Discretization](#1.2.1 Optimized Discretization)

[1.2.2 Knowledge Distillation](#1.2.2 Knowledge Distillation)

[1.2.3 Truncated Diffusion](#1.2.3 Truncated Diffusion)

[2. Improved Likelihood](#2. Improved Likelihood)

[2.1. Noise Schedule Optimization](#2.1. Noise Schedule Optimization)

[2.2. Reverse Variance Learning](#2.2. Reverse Variance Learning)

[2.3. Exact Likelihood Computation](#2.3. Exact Likelihood Computation)

[3. Data with Special Structures](#3. Data with Special Structures)

[3.1. Data with Manifold Structures](#3.1. Data with Manifold Structures)

[3.1.1 Known Manifolds](#3.1.1 Known Manifolds)

[3.1.2 Learned Manifolds](#3.1.2 Learned Manifolds)

[3.2. Data with Invariant Structures](#3.2. Data with Invariant Structures)

[3.3 Discrete Data](#3.3 Discrete Data)

[Application Taxonomy](#Application Taxonomy)

[1. Computer Vision](#1. Computer Vision)

[2. Natural Language Processing](#2. Natural Language Processing)

[3. Temporal Data Modeling](#3. Temporal Data Modeling)

[4. Multi-Modal Learning](#4. Multi-Modal Learning)

[5. Robust Learning](#5. Robust Learning)

[6. Molecular Graph Modeling](#6. Molecular Graph Modeling)

[7. Material Design](#7. Material Design)

[8. Medical Image Reconstruction](#8. Medical Image Reconstruction)

[Connections with Other Generative Models](#Connections with Other Generative Models)

[1. Variational Autoencoder](#1. Variational Autoencoder)

[2. Generative Adversarial Network](#2. Generative Adversarial Network)

[3. Normalizing Flow](#3. Normalizing Flow)

[4. Autoregressive Models](#4. Autoregressive Models)

[5. Energy-Based Models](#5. Energy-Based Models)


Algorithm Taxonomy

1. Efficient Sampling

1.1 Learning-Free Sampling
1.1.1 SDE Solver

Score-Based Generative Modeling through Stochastic Differential Equations

Adversarial score matching and improved sampling for image generation

Come-closer-diffuse-faster: Accelerating conditional diffusion models for inverse problems through stochastic contraction

Score-Based Generative Modeling with Critically-Damped Langevin Diffusion

Gotta Go Fast When Generating Data with Score-Based Models

Elucidating the Design Space of Diffusion-Based Generative Models

Generative modeling by estimating gradients of the data distribution

1.1.2 ODE Solver

Denoising Diffusion Implicit Models

gDDIM: Generalized denoising diffusion implicit models

Elucidating the Design Space of Diffusion-Based Generative Models

DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Step

Pseudo Numerical Methods for Diffusion Models on Manifolds

Fast Sampling of Diffusion Models with Exponential Integrator

Poisson flow generative models

1.2 Learning-Based Sampling
1.2.1 Optimized Discretization

Learning to Efficiently Sample from Diffusion Probabilistic Models

GENIE: Higher-Order Denoising Diffusion Solvers

Learning fast samplers for diffusion models by differentiating through sample quality

1.2.2 Knowledge Distillation

Progressive Distillation for Fast Sampling of Diffusion Models

Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed

1.2.3 Truncated Diffusion

Accelerating Diffusion Models via Early Stop of the Diffusion Process

Truncated Diffusion Probabilistic Models

2. Improved Likelihood

2.1. Noise Schedule Optimization

Improved denoising diffusion probabilistic models

Variational diffusion models

2.2. Reverse Variance Learning

Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models

Improved denoising diffusion probabilistic models

Stable Target Field for Reduced Variance Score Estimation in Diffusion Models

2.3. Exact Likelihood Computation

Score-Based Generative Modeling through Stochastic Differential Equations

Maximum likelihood training of score-based diffusion models

A variational perspective on diffusion-based generative models and score matching

Score-Based Generative Modeling through Stochastic Differential Equations

Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching

Maximum Likelihood Training of Implicit Nonlinear Diffusion Models

3. Data with Special Structures

3.1. Data with Manifold Structures
3.1.1 Known Manifolds

Riemannian Score-Based Generative Modeling

Riemannian Diffusion Models

3.1.2 Learned Manifolds

Score-based generative modeling in latent space

Diffusion priors in variational autoencoders

Hierarchical text-conditional image generation with clip latents

High-resolution image synthesis with latent diffusion models

3.2. Data with Invariant Structures

GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation

Permutation invariant graph generation via score-based generative modeling

Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

DiGress: Discrete Denoising diffusion for graph generation

Learning gradient fields for molecular conformation generation

Graphgdp: Generative diffusion processes for permutation invariant graph generation

SwinGNN: Rethinking Permutation Invariance in Diffusion Models for Graph Generation

3.3 Discrete Data

Vector quantized diffusion model for text-to-image synthesis

Structured Denoising Diffusion Models in Discrete State-Spaces

Vector Quantized Diffusion Model with CodeUnet for Text-to-Sign Pose Sequences Generation

Deep Unsupervised Learning using Non equilibrium Thermodynamics.

A Continuous Time Framework for Discrete Denoising Models

Application Taxonomy

1. Computer Vision

2. Natural Language Processing

3. Temporal Data Modeling

4. Multi-Modal Learning

5. Robust Learning

6. Molecular Graph Modeling

7. Material Design

8. Medical Image Reconstruction

Connections with Other Generative Models

1. Variational Autoencoder

2. Generative Adversarial Network

3. Normalizing Flow

4. Autoregressive Models

5. Energy-Based Models

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