Preserve the Hard, Regenerate the Rest: Uncertainty-Guided Synthetic Training Data Augmentation with Diffusion Models
arXiv:2606.31603v1 Announce Type: cross Abstract: Semantic segmentation models struggle with data sparsity and rare or visually diverse regions, e.g., dense regions or small objects in aerial or autonomous mobility data. While synthetic augmentation is an appealing solution, directly generating new labeled data risks misalignment of labels and generated pixels. Existing solutions to this problem often rely on external models, or employ coarse heuristics such as indiscriminately augmenting all foreground objects or entire backgrounds, which wastes capacity on uninformative pixels. To address th
The increasing sophistication of generative AI, particularly diffusion models, alongside the persistent challenge of data sparsity in specialized fields like semantic segmentation, enables new approaches to synthetic data generation.
Improving the quality and targeted generation of synthetic training data directly addresses a key bottleneck in AI model development, especially for robust performance in real-world, complex scenarios with rare or visually diverse features.
The ability to generate more precise and uncertainty-guided synthetic data reduces reliance on extensive human labeling, accelerates model training, and potentially allows for the deployment of AI in data-scarce domains previously deemed impractical.
- · AI model developers
- · Autonomous vehicle industry
- · Aerial imaging and analysis companies
- · Diffusion model providers
- · Traditional data annotation services
- · AI developers reliant solely on real-world datasets
More robust and generalizable AI models for semantic segmentation tasks, especially in challenging environments.
Reduced costs and accelerated development cycles for AI applications requiring high-fidelity scene understanding.
Enhanced AI capabilities in critical infrastructure, defense, and environmental monitoring where rare event detection is paramount.
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Read at arXiv cs.LG