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Workshop: NeurIPS 2022 Workshop on Score-Based Methods

First hitting diffusion models

Mao Ye · Lemeng Wu · Qiang Liu


We propose a family of First Hitting Diffusion Models (FHDM), deep generative models that generate data with a diffusion process that terminates at a random first hitting time. This yields an extension of the standard fixed-time diffusion models that terminate at a pre-specified deterministic time. Although standard diffusion models are designed for continuous unconstrained data, FHDM is naturally designed to learn distributions on continuous as well as a range of discrete and structure domains.

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