Diffusion Models: A Comprehensive Survey of Methods and Applications
Introduction to the Project
The project "Diffusion Models: A Comprehensive Survey of Methods and Applications" centers around a repository curated for the collection and categorization of scholarly papers related to diffusion models. The cornerstone of this project is a survey paper that has been accepted by the esteemed ACM Computing Surveys journal. As the field of diffusion models is undergoing rapid development, this repository and the accompanying arxiv paper are continuously updated to encapsulate the latest advancements and research findings.
Overview of Diffusion Models
The project provides an extensive overview of diffusion models, which are cutting-edge models used primarily for generative tasks. These models work by progressively transforming a simple distribution, like Gaussian noise, into a complex target distribution, often images or other types of data. Central to the project are guidelines on enhancing the efficiency of sampling methods, improving likelihood computation, and dealing with data that possess special structures.
Algorithm Taxonomy
The project classifies algorithms into several key areas:
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Sampling-Acceleration Enhancement: Focuses on improving the efficiency of generating samples. It includes methods like:
- Learning-Free Sampling: Includes Solvers for Stochastic Differential Equations (SDE) and Ordinary Differential Equations (ODE).
- Learning-Based Sampling: Techniques like Optimized Discretization and Knowledge Distillation are highlighted to make sampling faster and more efficient.
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Likelihood-Maximization Enhancement: Centers on maximizing the likelihood of generated samples, using techniques such as:
- Noise Schedule Optimization
- Reverse Variance Learning
- Exact Likelihood Computation
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Data with Special Structures: Addresses handling of data with inherent complex structures:
- Known and Learned Manifold Structures
- Invariant and Discrete Data Structures
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Diffusion with (Multimodal) Large Language Models (LLM): Explores enhancing diffusion models via simple combinations or deep collaborations with large language models.
Application Taxonomy
The project delineates various applications of diffusion models across multiple domains:
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Computer Vision: The models are instrumental in tasks such as image enhancement (super-resolution, inpainting), semantic segmentation, and video generation.
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Natural Language Processing: Although specific subcategories are not detailed in the excerpt, diffusion models find applications in language tasks, often through integration with other modalities.
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Temporal Data Modeling: Involves tasks around time-series data, including imputation and forecasting, with applications in signal processing.
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Multi-Modal Learning: Explores generating content across different modalities using diffusion models, such as from text to image or video.
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Robust Learning: Enhances the robustness of models by generating synthetic data or purifying datasets.
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Specialized Domains: Includes molecular graph modeling, material design, and medical image reconstruction, where diffusion models provide novel solutions to intricate problems.
Connections with Other Generative Models
The project acknowledges connections with other types of generative models, including Variational Autoencoders, Generative Adversarial Networks, Normalizing Flow models, Autoregressive Models, and Energy-Based Models. These discussions provide insights into how diffusion models compare and contrast with traditional methods.
Conclusion
"Diffusion Models: A Comprehensive Survey of Methods and Applications" is an extensive resource for researchers and practitioners in the field of machine learning and data science. The repository not only provides classifications and applications of diffusion models but also bridges connections with other prominent generative frameworks. Continuously updated, it stays relevant amidst the field's rapid advancements.