Related papers: Fair Sampling in Diffusion Models through Switching Mechanism

Fair Sampling in Diffusion Models through Switching Mechanism

URL: http://arxiv.org/abs/2401.03140v5
Date: Thu, 03 Oct 2024 03:59:37 GMT
Title: Fair Sampling in Diffusion Models through Switching Mechanism
Authors: Yujin Choi, Jinseong Park, Hoki Kim, Jaewook Lee, Saerom Park,
Abstract summary: We propose a fairness-aware sampling method called textitattribute switching mechanism for diffusion models. We mathematically prove and experimentally demonstrate the effectiveness of the proposed method on two key aspects.
Score: 5.560136885815622
License:
Abstract: Diffusion models have shown their effectiveness in generation tasks by well-approximating the underlying probability distribution. However, diffusion models are known to suffer from an amplified inherent bias from the training data in terms of fairness. While the sampling process of diffusion models can be controlled by conditional guidance, previous works have attempted to find empirical guidance to achieve quantitative fairness. To address this limitation, we propose a fairness-aware sampling method called \textit{attribute switching} mechanism for diffusion models. Without additional training, the proposed sampling can obfuscate sensitive attributes in generated data without relying on classifiers. We mathematically prove and experimentally demonstrate the effectiveness of the proposed method on two key aspects: (i) the generation of fair data and (ii) the preservation of the utility of the generated data.

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