Noise Injection

Noise injection, the deliberate introduction of noise into various stages of machine learning processes, aims to improve model robustness, generalization, and privacy. Current research focuses on optimizing noise types and injection strategies within diverse architectures, including deep neural networks, generative models like StyleGAN-2, and federated learning frameworks, often employing techniques like adaptive noise injection and anticorrelated noise. These advancements are significant because they enhance model performance, particularly in challenging scenarios with noisy data or adversarial attacks, and contribute to the development of more reliable and privacy-preserving AI systems.