AugGen: Synthetic Augmentation Can Improve Discriminative Models

• URL: http://arxiv.org/abs/2503.11544v2
• Date: Wed, 11 Jun 2025 12:32:30 GMT
• Title: AugGen: Synthetic Augmentation Can Improve Discriminative Models
• Authors: Parsa Rahimi, Damien Teney, Sebastien Marcel,
• Abstract summary: Synthetic data generation offers a promising alternative to external datasets or pre-trained models.<n>In this paper, we introduce AugGen, a self-contained synthetic augmentation technique.<n>Our findings demonstrate that carefully integrated synthetic data can both mitigate privacy constraints and substantially enhance discriminative performance in face recognition.
• Score: 14.680260279598045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing reliance on large-scale datasets in machine learning poses significant privacy and ethical challenges, particularly in sensitive domains such as face recognition (FR). Synthetic data generation offers a promising alternative; however, most existing methods depend heavily on external datasets or pre-trained models, increasing complexity and resource demands. In this paper, we introduce AugGen, a self-contained synthetic augmentation technique. AugGen strategically samples from a class-conditional generative model trained exclusively on the target FR dataset, eliminating the need for external resources. Evaluated across 8 FR benchmarks, including IJB-C and IJB-B, our method achieves 1-12% performance improvements, outperforming models trained solely on real data and surpassing state-of-the-art synthetic data generation approaches, while using less real data. Notably, these gains often exceed those from architectural modifications, underscoring the value of synthetic augmentation in data-limited scenarios. Our findings demonstrate that carefully integrated synthetic data can both mitigate privacy constraints and substantially enhance discriminative performance in face recognition. Paper website: https://parsa-ra.github.io/auggen/.

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