Abstract:

Flow matching provides a scalable framework for training continuous normalizing flows and has consequently gained popularity in generative modelling. Its close connection to continuous-time dynamics has also made it increasingly relevant to scientific applications, including cellular trajectory inference, weather forecasting, and the modelling of physical systems.

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Diffusion models are now the dominant approach for high-fidelity image generation, yet they remain highly vulnerable to privacy attacks, including reconstruction and membership inference attacks (e.g.,