Real World Perturbation

Real-world perturbation research investigates how machine learning models, particularly deep learning models, respond to variations and uncertainties present in real-world data. Current efforts focus on evaluating model robustness across diverse perturbation types, including environmental changes (e.g., lighting, object properties) in robotics and noise or adversarial attacks in medical imaging and natural language processing. This research is crucial for improving the reliability and generalizability of AI systems, ensuring their performance is consistent and trustworthy across different conditions and preventing failures due to unexpected inputs. The ultimate goal is to develop more robust and reliable AI models applicable to real-world scenarios.