Abstract
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 page this https URL
Abstract (translated)
在机器学习中，对大规模数据集的依赖日益增加，这带来了重大的隐私和伦理挑战。合成数据生成提供了一种有前景的解决方案；然而，大多数当前方法依赖于外部数据集或预训练模型，这增加了复杂性并加剧了资源需求。在这项工作中，我们介绍了一种新颖的自包含合成增强技术，该技术战略性地从仅在目标数据集上进行训练的条件生成模型中采样。这种策略消除了对辅助数据源的需求。当应用于面部识别数据集时，我们的方法在IJB-C和IJB-B基准测试上实现了1-12%的性能提升。它不仅优于只使用真实数据训练的模型，还超过了最先进的合成数据生成基线表现。值得注意的是，这些改进往往超越了架构改进所带来的效果，突显了在数据稀缺环境下合成增强的重要影响。这些发现表明，精心整合的合成数据不仅可以解决隐私和资源限制问题，还能显著提升模型性能。项目页面：[此链接](请在此处插入实际链接)
URL
https://arxiv.org/abs/2503.11544
