AugGen: Synthetic Augmentation Can Improve Discriminative Models

• URL: http://arxiv.org/abs/2503.11544v1
• Date: Fri, 14 Mar 2025 16:10:21 GMT
• Title: AugGen: Synthetic Augmentation Can Improve Discriminative Models
• Authors: Parsa Rahimi, Damien Teney, Sebastien Marcel,
• Abstract summary: We introduce a novel self-contained synthetic augmentation technique.<n>It strategically samples from a conditional generative model trained exclusively on the target dataset.<n>It achieves 1--12% performance improvements on the IJB-C and IJB-B benchmarks.
• Score: 14.680260279598045
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://parsa-ra.github.io/auggen

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