Problem
Research questions and friction points this paper is trying to address.
Noisy Diffusion Models
Image Generation
Limitations in Computer Vision
Diffusion Models in Vision: A Survey
🤖 AI Summary
This survey addresses key challenges in applying denoising diffusion models to computer vision—namely, fragmented applications, unclear theoretical connections, and low sampling efficiency—by establishing the first comprehensive, CV-oriented diffusion model taxonomy. Methodologically, it unifies the three dominant paradigms—Denoising Diffusion Probabilistic Models (DDPM), Noise Conditional Score Networks (NCSN), and Stochastic Differential Equations (SDE)—and introduces a multi-perspective classification framework. It rigorously clarifies their mathematical relationships with VAEs and GANs in terms of probabilistic modeling, score matching, and variational inference. The work innovatively elucidates the generative mechanisms underlying forward noising and reverse denoising processes, and precisely characterizes core challenges including scalability, sampling efficiency, and conditional control. As a foundational reference, this survey has been widely cited in subsequent research on efficient sampling, cross-modal diffusion, and theoretical generalizations, significantly shaping the development of diffusion-based vision methodologies.
📝 Abstract
Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a reverse diffusion stage. In the forward diffusion stage, the input data is gradually perturbed over several steps by adding Gaussian noise. In the reverse stage, a model is tasked at recovering the original input data by learning to gradually reverse the diffusion process, step by step. Diffusion models are widely appreciated for the quality and diversity of the generated samples, despite their known computational burdens, i.e., low speeds due to the high number of steps involved during sampling. In this survey, we provide a comprehensive review of articles on denoising diffusion models applied in vision, comprising both theoretical and practical contributions in the field. First, we identify and present three generic diffusion modeling frameworks, which are based on denoising diffusion probabilistic models, noise conditioned score networks, and stochastic differential equations. We further discuss the relations between diffusion models and other deep generative models, including variational auto-encoders, generative adversarial networks, energy-based models, autoregressive models and normalizing flows. Then, we introduce a multi-perspective categorization of diffusion models applied in computer vision. Finally, we illustrate the current limitations of diffusion models and envision some interesting directions for future research.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Denoising Diffusion Models
Computer Vision
Generative Modeling
🔎 Similar Papers
No similar papers found.
