Continuous diffusion for categorical data

• URL: http://arxiv.org/abs/2211.15089v1
• Date: Mon, 28 Nov 2022 06:08:54 GMT
• Title: Continuous diffusion for categorical data
• Authors: Sander Dieleman, Laurent Sartran, Arman Roshannai, Nikolay Savinov, Yaroslav Ganin, Pierre H. Richemond, Arnaud Doucet, Robin Strudel, Chris Dyer, Conor Durkan, Curtis Hawthorne, R\'emi Leblond, Will Grathwohl, Jonas Adler
• Abstract summary: We propose CDCD, a framework for modelling categorical data with diffusion models that are continuous both in time and input space. We demonstrate its efficacy on several language modelling tasks.
• Score: 42.60475010640669
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement. Their success hinges on the fact that the underlying physical phenomena are continuous. For inherently discrete and categorical data such as language, various diffusion-inspired alternatives have been proposed. However, the continuous nature of diffusion models conveys many benefits, and in this work we endeavour to preserve it. We propose CDCD, a framework for modelling categorical data with diffusion models that are continuous both in time and input space. We demonstrate its efficacy on several language modelling tasks.

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