Unverified70% confidenceFactExact time
Yang Song et al. unified the diffusion process under the framework of continuous-time stochastic differential equations and proposed score-based generative models.
2
Sources
70%
Confidence
Long-term
Relevance
7/14/2026
First Seen
Sources
引导式生成模型：精准估算极端事件概率的新方法
Related Claims
UnverifiedThe theoretical foundation of diffusion models was unified by Song Yang et al. in 2020 through a stochastic differential equation (SDE) framework; the discovery of probability flow ODEs enabled deterministic sampling and gave rise to accelerated sampling methods such as DDIM, compressing the number of sampling steps from 1000 to 10–50.77% similarUnverifiedThe denoising sampling process of a diffusion model can be regarded as the solution of a stochastic differential equation, comprising a deterministic drift term and a stochastic diffusion term.70% similarUnverified2020年后兴起的扩散模型（Diffusion Models）直接建立在随机微分方程与概率流的数学框架之上70% similarUnverifiedAutoregressive diffusion is a fusion variant built on diffusion models that introduces conditional dependence on historical time steps at each step of the reverse denoising process, causing the generation process to unfold along the time axis.69% similarUnverifiedThe technical foundations of diffusion models originate from stochastic processes in non-equilibrium thermodynamics, comprising two stages: a forward diffusion process and a reverse denoising process.68% similar
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