AugGen: Synthetic Augmentation Can Improve Discriminative Models

The increasing dependence on large-scale datasets in machine learning introduces significant privacy and ethical challenges. Synthetic data generation offers a promising solution; however, most current methods rely on external datasets or pre-trained models, which add complexity and escalate resource demands. In this work, we introduce a novel self-contained synthetic augmentation technique that strategically samples from a conditional generative model trained exclusively on the target dataset. This approach eliminates the need for auxiliary data sources. Applied to face recognition datasets, our method achieves 1--12\% performance improvements on the IJB-C and IJB-B benchmarks. It outperforms models trained solely on real data and exceeds the performance of state-of-the-art synthetic data generation baselines. Notably, these enhancements often surpass those achieved through architectural improvements, underscoring the significant impact of synthetic augmentation in data-scarce environments. These findings demonstrate that carefully integrated synthetic data not only addresses privacy and resource constraints but also substantially boosts model performance. Project pagethis https URL

@article{rahimi2025_2503.11544,
  title={ AugGen: Synthetic Augmentation Can Improve Discriminative Models },
  author={ Parsa Rahimi and Damien Teney and Sebastien Marcel },
  journal={arXiv preprint arXiv:2503.11544},
  year={ 2025 }
}