Boosting Statistic Learning with Synthetic Data from Pretrained Large Models
This work addresses the challenge of effectively augmenting data for statistic learning, though it is incremental as it builds on existing generative models and focuses on selective integration.
The paper tackles the problem of using synthetic data from generative models like Stable Diffusion to enhance predictive modeling by proposing an end-to-end framework that generates and filters data through domain-specific statistical methods, resulting in consistent improvements in predictive performance across various settings.
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.