mlsanityData to 7 Oct 2026

Boosting Statistic Learning with Synthetic Data from Pretrained Large Models

arXiv 2505.04992 ↗First posted 8 May 2025stat.ML cs.LG stat.APcited by 0

On the map: Diffusion models → Diffusion foundations and sampling

Abstract (arXiv)

The rapid advancement of generative models, such as Stable Diffusion, raises a key question: how can synthetic data from these models enhance predictive modeling? While they can generate vast amounts of datasets, only a subset meaningfully improves performance. We propose a novel end-to-end framework that generates and systematically filters synthetic data through domain-specific statistical methods, selectively integrating high-quality samples for effective augmentation. Our experiments demonstrate consistent improvements in predictive performance across various settings, highlighting the potential of our framework while underscoring the inherent limitations of generative models for data augmentation. Despite the ability to produce large volumes of synthetic data, the proportion that effectively improves model performance is limited.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models. Builds on 78 of its 172 references that are in the map. First posted on arXiv 8 May 2025.

Builds on

78 of 172 references.

Closest ancestors · 4

Same direction, newest first

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  2. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis4 Jul
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  4. 2020Score-Based Generative Modeling through Stochastic Differential Equations26 Nov

Other references · 74

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