arXiv:2509.24710v2 Announce Type: replace-cross Abstract: Score-based diffusion models are a highly effective method for generating samples from a distribution of images. We consider scenarios where the training data comes from a noisy version of the target distribution, and present an efficiently implementable modification of the inference procedure to generate noiseless samples. Our approach is motivated by the manifold hypothesis, according to which meaningful data is concentrated around some low-dimensional manifold of a high-dimensional ambient space. The central idea is that noise manife
The continuous evolution of diffusion models necessitates improvements in handling noisy training data to enhance their real-world applicability.
This development can significantly improve the quality and efficiency of generative AI, particularly in fields where clean data is scarce or expensive, leading to more robust and reliable AI systems.
The ability to generate noiseless samples from noisy training data efficiently changes the prior limitations on data quality for effective diffusion model training.
- · AI researchers
- · Generative AI developers
- · Industries with noisy datasets
- · AI models reliant on strictly clean data
Improved generative AI models that are more resilient to real-world data imperfections.
Faster development and deployment of AI applications in sectors like medical imaging or autonomous driving where data noise is prevalent.
Reduced data curation costs for AI development, potentially democratizing access to advanced generative AI capabilities.
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Read at arXiv cs.LG