Score-based generative diffusion with "active" correlated noise sources

• URL: http://arxiv.org/abs/2411.07233v1
• Date: Mon, 11 Nov 2024 18:51:08 GMT
• Title: Score-based generative diffusion with "active" correlated noise sources
• Authors: Alexandra Lamtyugina, Agnish Kumar Behera, Aditya Nandy, Carlos Floyd, Suriyanarayanan Vaikuntanathan,
• Abstract summary: Diffusion models exhibit robust generative properties by approximating the underlying distribution of a dataset. In this work, we explore how the generative performance may be modulated if noise sources with temporal correlations are used for the destruction of the data.
• Score: 38.32962448223814
• License:
• Abstract: Diffusion models exhibit robust generative properties by approximating the underlying distribution of a dataset and synthesizing data by sampling from the approximated distribution. In this work, we explore how the generative performance may be be modulated if noise sources with temporal correlations -- akin to those used in the field of active matter -- are used for the destruction of the data in the forward process. Our numerical and analytical experiments suggest that the corresponding reverse process may exhibit improved generative properties.

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