Our guest speaker is Yang Song from the University of Stanford and you are all cordially invited to the CVG Seminar on June 24th at 5:30 p.m. CET on Zoom (passcode is 299064), where‪ Yang will give a talk titled “Generative Modeling by Estimating Gradients of the Data Distribution“.

Abstract

Existing generative methods are typically based on training explicit probability representations with maximum likelihood (e.g., VAEs), or learning implicit sampling procedures with adversarial training (e.g., GANs). The former requires variational inference or special model architectures for tractable training, while the latter can be unstable. To address these difficulties, we explore an alternative approach based on estimating gradients of probability densities. We can estimate gradients of distributions by training flexible neural network models with denoising score matching, and use these models for sample generation, exact likelihood computation, posterior inference, and data manipulation by leveraging techniques of MCMC and stochastic differential equations. Our framework enables free-form model architectures, requires no adversarial optimization, and achieves the state-of-the-art performance in many applications such as image and audio generation.

Bio