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October 20, 2025Open Access

More Data or Better Algorithms: Latent Diffusion Augmentation for Deep Imbalanced Regression

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This study proposes LatentDiff to enhance prediction of minority labels in deep imbalanced regression, indicating improved data handling.

LatentDiff significantly enhances predictions for minority labels in high-dimensional regression tasks, improving accessibility.
Experimental results show that LatentDiff improves minority region predictions while maintaining overall accuracy across three benchmarks.
The proposed framework applies conditional diffusion models to generate high-quality features from latent representations efficiently.
This innovation addresses a critical gap in deep imbalanced regression methodologies, where previous solutions were inadequate.

Shayan Alahyari (2025) studied this question.

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