Adaptive Adversarial Augmentation for Controllable Face Synthesis

Key points

• Synthetic data provides a scalable alternative to real-world datasets for training face recognition models, particularly under challenging conditions such as low resolution, occlusion, and masks.1
• Recognition models trained on EFCS data consistently outperform baselines across multiple benchmarks, showing improved generalization to real-world scenarios.1
• Furthermore, we introduce an analytically motivated formulation linking perturbation-induced difficulty, sample utility, and performance degradation, offering principled insights into balancing synthetic data complexity for optimal training.1
• Together, these contributions establish EFCS as an effective and analytically grounded approach for bridging the gap between synthetic and real datasets.1

Sources (1)

• [1]Adaptive Adversarial Augmentation for Controllable Face Synthesis
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 06:48 AM

  We propose Ensemble Feedback Controllable Synthesis (EFCS), a guided framework that generates diverse and challenging samples while preserving visual realism.

  Synthetic data provides a scalable alternative to real-world datasets for training face recognition models, particularly under challenging conditions such as low resolution, occlusion, and masks.

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