arXiv:2606.27696v1 Announce Type: new Abstract: In this paper, we are the first to examine the correlations between class frequency and the multi-scale noise schedule within diffusion models. For score-based generative models, low-density regions often lead to inaccurately estimated scores, thereby compromising the generation quality. Although the multi-scale noise schedule can alleviate this issue during the diffusion process, low-frequency classes still face the challenge of large low-density regions, resulting in more inaccurate estimated scores than high-frequency classes. Furthermore, hig
This research emerges as diffusion models become a dominant paradigm in generative AI, necessitating refined control and quality for diverse applications.
Improved diffusion model performance, especially for 'low-frequency' or rare data, is critical for robust and unbiased AI systems across various domains.
Diffusion models can now be fine-tuned to more effectively handle imbalanced datasets, leading to higher quality and more reliable generative outputs.
- · AI researchers
- · Generative AI companies
- · Data scientists
- · Industries relying on AI for diverse data generation
- · Models reliant on vast, balanced datasets
- · Generative AI with bias issues
Enhances the ability of diffusion models to generate high-quality data from underrepresented classes, reducing bias.
Accelerates the adoption of generative AI in applications where data scarcity or class imbalance was previously a significant hurdle.
Could lead to more equitable and representative AI applications, fostering broader societal acceptance and utility.
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Read at arXiv cs.LG