Perturbation Augmentation

Perturbation augmentation is a data augmentation technique enhancing machine learning model performance by strategically introducing controlled noise or variations into training data. Current research focuses on adaptive methods that dynamically adjust perturbation magnitudes based on model performance or data characteristics, employing techniques like reinforcement learning and entropy-based approaches within diverse model architectures including diffusion models and graph neural networks. This technique improves model generalization, robustness, and fairness, impacting fields ranging from image classification and 3D modeling to high-energy physics and natural language processing by mitigating overfitting and bias while improving accuracy and efficiency.