作为生成模型的一种,score-based model 也遵循学习+采样的范式,其学习过程使用 score matching 来间接学习分布,采样过程使用 Langevin dynamics 通过迭代过程进行采样(和 diffusion models 的采样过程有点类似)。在训练时由于低概率密度区域会有比较低的权重,所以这部分区域无法准确学习,为了解决这个问题,又使用multiple n...
Score-Based Generative Modeling through Stochastic Differential Equations (SDE) 妖妖 3D AIGC/Reconstruction/LM 73 人赞同了该文章SDE 这篇大名鼎鼎的将扩散模型和SDE(随机微分方程)结合起来的工作,同样一作为宋飏博士,为 ICLR 2021 Outstanding Paper Award(这个奖一共8篇paper)。这篇工作就是将之前介绍过的NCSN...
and Poole B. Score-based generative modeling through stochastic differential equations. In International Conference on Learning Representations (ICLR), 2021概从stochastic differential equation (SDE) 角度看 diffusion models.符号说明x(t),t∈[0,T]x(t),t∈[0,T] 为xx 在时间 tt 的一个状态; pt(x...
Score-based generative models show good performance recently inimage generation. In the context of statistics, Score is defined as the gradient of logarithmic probability density with respect to the data distribution parameter. Usually, while training agenerative model, noises are added to the original...
We propose a unified framework that generalizes and improves previous work on score-based generative models through the lens of stochastic differential equations (SDEs). In particular, we can transform data to a simple noise distribution with a continuous-time stochastic process described by an SDE....
We propose a unified framework that generalizes and improves previous work on score-based generative models through the lens of stochastic differential equations (SDEs). In particular, we can transform data to a simple noise distribution with a continuous-time stochastic process described by an SDE....
Also, due to its generative nature, our approach can quantify uncertainty, which is not possible with standard regression settings. On top of all the advantages, our method also has very strong performance, even beating the models trained with full supervision. With extensive experiments, we ...
Diffusion Models 扩散模型 数学解释 _小闪电_ 14:16 【研1基本功 (真的很简单)Diffusion Model】完成扩散模型!!结尾有bonus!! happy魇 12:03 扩散模型 Diffusion Model 3-2 SDE(一) VictorYuki 00:14 其实我还没准备好 一粉田英 410.8万133 22:18 ...
这篇文章通过将得分匹配方法与随机微分方程(SDE)相结合,成功地将得分匹配生成模型(Score-Based Generative Models)与扩散概率模型(DDPM)统一在了一个共同的理论框架,引入SDE形式来描述扩散模型的本质好处:通过使用 SDE,提高模型的可解释性,也可以利用许多现有的强大数学工具和理论对SDE进行数值计算。
更重要的是,通过把“不同尺度扰动噪声的数量”扩展到无穷个,可以发现 diffusion models 和 score-based generative models(如 NCSN)都可以看作是由 score function 决定的 SDE 的离散形式,从而实现了它们理论上的统一。 SDE 统一视角 下面详细介绍下,在 SDE 视角下,NCSN 和 DDPM 对应的 SDE 具体形式及其数学...