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0:00 Introduction
0:11 Creating noise from data is easy
0:27 Creating data from noise is generative modeling
0:49 Perturbing data with a stochastic differential equatio
3:34 Estimating score functions from data
7:45 Probability flow ODE Turning the SDE to an ODE without affecting P(x)
8:33 Probability flow ODEs as continuous normalizing flow Probability flow ODE is an instance of Neural ODE Chen et al. 2018
9:42 Solving Reverse-ODE for Sampling
11:54 Controllable Generation: class-conditional generation
13:04 Controllable Generation: colorization
13:37 Conclusion
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Score-based generative modeling is a powerful technique that has gained popularity in recent years due to its ability to model complex and high-dimensional data. In this video, we will explore how stochastic differential equations (SDEs) can be used to perform score-based generative modeling, and their advantages over other methods.
We will first introduce the concept of score-based generative modeling and explain how it works. We will then delve into the basics of stochastic differential equations and how they can be used to model the score function of a probability distribution. We will also demonstrate how to solve SDEs numerically using different techniques, such as the Euler-Maruyama method and the Runge-Kutta method.
Additionally, we will discuss the benefits of SDE-based score estimation, including improved robustness to noise and the ability to handle non-smooth score functions. We will also showcase several real-world applications of SDE-based score estimation, including image generation and density estimation.
Whether you are a researcher, student, or industry practitioner, this video will provide you with a comprehensive understanding of score-based generative modeling through stochastic differential equations and its applications.
Tags: score-based generative modeling, stochastic differential equations, SDEs, probability distribution, Euler-Maruyama method, Runge-Kutta method, image generation, density estimation.
Keywords: Score-based generative modeling, stochastic differential equations, SDEs, probability distribution, Euler-Maruyama method, Runge-Kutta method, image generation, density estimation, machine learning, deep learning, artificial intelligence, research, modeling.
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