Talks

Flow-based generative models

Abstract
This talk aims to present generative models based on flows, which are currently state-of-the-art methods in generative AI for continuous spaces. We will begin with a brief overview of energy-based models, one of the earliest approaches to model probability distributions, which provide the basic ideas for understanding the difficulties of data generation. We will then focus on flow-based models and their efficient training through flow matching, while also explaining their connections to the well-known diffusion models. Particular emphasis will be placed on how optimal transport can serve as a framework for defining dynamic flows in probability space.