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About this paper
Lin-Chun Huang, Ching Chieh Tsao, Fang-Yi Su, Jung-Hsien Chiang
National Cheng Kung University's Debiasing Diffusion Model (DDM) mitigates algorithmic bias in Stable Diffusion models by integrating a latent representation learning indicator, allowing it to decouple sensitive attributes from learned representations without relying on predefined attribute conditions. The framework reduces bias in both unconditioned and conditioned generation tasks, balancing representation fairness with some trade-off in image quality.
Key insights
- Effective bias mitigation in generative models is achievable by promoting attribute-agnostic latent representation learning, moving beyond the reliance on explicit sensitive attribute conditioning.
- There is a persistent trade-off between improving fairness and maintaining generated image quality or diversity in diffusion models.
- An integrated regularization signal, in the form of a latent representation learning indicator, can reshape a model's latent space during fine-tuning to encourage more equitable outputs.
Summary by alphaXiv.
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